Revision of the Australian Ozestheria fauna
Manja Hethke and Martin Schwentner
26 June 2024
1 Preparation
1.1 Loading FFT-coefficients
shape_8 <- read.csv("Condyle_lang_vs2.csv", header=T, sep=";")1.2 Data subsets
A_shape <- shape_8[which(shape_8$Species2 == 'A'), ]
B_shape <- shape_8[which(shape_8$Species2 == 'B'), ]
E_shape <- shape_8[which(shape_8$Species2 == 'E'), ]
H1_shape <- shape_8[which(shape_8$Species2 == 'H1'), ]
H2_shape <- shape_8[which(shape_8$Species2 == 'H2'), ]
I_shape <- shape_8[which(shape_8$Species2 == 'I'), ]
K_shape <- shape_8[which(shape_8$Species2 == 'K'), ]
mariae_shape <- shape_8[which(shape_8$Species2 == 'mariae'), ]
minor_shape <- shape_8[which(shape_8$Species2 == 'minor'), ]
N_shape <- shape_8[which(shape_8$Species2 == 'N'), ]
O_shape <- shape_8[which(shape_8$Species2 == 'O'), ]
P_shape <- shape_8[which(shape_8$Species2 == 'P'), ]
Q1_shape <- shape_8[which(shape_8$Species2 == 'Q1'), ]
Q3_shape <- shape_8[which(shape_8$Species2 == 'Q3'), ]
Q4_shape <- shape_8[which(shape_8$Species2 == 'Q4'), ]
Q5_shape <- shape_8[which(shape_8$Species2 == 'Q5'), ]
R_shape <- shape_8[which(shape_8$Species2 == 'R'), ]
S_shape <- shape_8[which(shape_8$Species2 == 'S'), ]
typica_shape <- shape_8[which(shape_8$Species2 == 'typica'), ]
U_shape <- shape_8[which(shape_8$Species2 == 'U'), ]
W_shape <- shape_8[which(shape_8$Species2 == 'W'), ]
weeksi_shape <- shape_8[which(shape_8$Species2 == 'weeksi'), ]
X2_shape <- shape_8[which(shape_8$Species2 == 'X2'), ]
X3_shape <- shape_8[which(shape_8$Species2 == 'X3'), ]
X4_shape <- shape_8[which(shape_8$Species2 == 'X4'), ]
X5_shape <- shape_8[which(shape_8$Species2 == 'X5'), ]
X7_shape <- shape_8[which(shape_8$Species2 == 'X7'), ]
X8_shape <- shape_8[which(shape_8$Species2 == 'X8'), ]
X9_shape <- shape_8[which(shape_8$Species2 == 'X9'), ]
typica_mean <- cbind("typica", as.data.frame(t(colMeans(shape_8[c(492:496), c(2:15)]))),"typica_mean","typica_mean", "mean")
colnames(typica_mean)[1:18] <- c("Collection","A2","A3","A4","A5","A6","A7","A8","B2","B3","B4","B5","B6","B7","B8","Species","Species2","Sex")
minor_mean <- cbind("minor", as.data.frame(t(colMeans(shape_8[c(487:491), c(2:15)]))),"minor_mean","minor_mean", "mean")
colnames(minor_mean)[1:18] <- c("Collection","A2","A3","A4","A5","A6","A7","A8","B2","B3","B4","B5","B6","B7","B8","Species","Species2","Sex")
mariae_mean <- cbind("mariae", as.data.frame(t(colMeans(shape_8[c(399:403), c(2:15)]))),"mariae_mean","mariae_mean", "mean")
colnames(mariae_mean)[1:18] <- c("Collection","A2","A3","A4","A5","A6","A7","A8","B2","B3","B4","B5","B6","B7","B8","Species","Species2","Sex")
All <- as.data.frame(rbind(A_shape, B_shape, E_shape, H1_shape, H2_shape, I_shape, K_shape, N_shape, O_shape, P_shape, Q1_shape, Q3_shape, Q4_shape, Q5_shape, R_shape, S_shape, U_shape, W_shape, weeksi_shape, X2_shape, X3_shape, X4_shape, X5_shape, X7_shape, X8_shape, X9_shape))
All_singletons <- as.data.frame(rbind(A_shape, B_shape, E_shape, H1_shape, H2_shape, I_shape, K_shape, N_shape, O_shape, P_shape, Q3_shape, Q4_shape, Q5_shape, R_shape, S_shape, weeksi_shape, X2_shape, X3_shape, X4_shape, X5_shape, X8_shape, X9_shape)) #for typicality; minus singletons Q1,U,W,X7
All_ohne_Q <- as.data.frame(rbind(A_shape, B_shape, E_shape, H1_shape, H2_shape, I_shape, K_shape, N_shape, O_shape, P_shape, R_shape, S_shape, U_shape, W_shape, X2_shape, X3_shape, X4_shape, X5_shape, X7_shape, X8_shape, X9_shape))
Q_K_typica <- as.data.frame(rbind(K_shape, Q3_shape, Q4_shape, Q5_shape, typica_shape, typica_mean))
B_H2_O_S_X8_minor <- as.data.frame(rbind(B_shape, H2_shape, O_shape, S_shape, X8_shape, minor_shape, minor_mean))
K_N_X5_mariae <- as.data.frame(rbind(K_shape, N_shape, X5_shape, mariae_shape, mariae_mean))1.3 Distribution and correlation analyses
Pearson product moment correlation coefficient, following SOGA page 19/104.
1.3.1 Full set
pairs.panels(All[2:15], smooth = F, ellipses = T)
1.3.2 Species distribution and correlation
1.3.2.1 A
pairs.panels(A_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species A')
1.3.2.2 B
pairs.panels(B_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species B')
1.3.2.3 E
pairs.panels(E_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species E')
1.3.2.4 H1
pairs.panels(H1_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species H1')
1.3.2.5 H2
pairs.panels(H2_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species H2')
1.3.2.6 I
pairs.panels(I_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species I')
1.3.2.7 K
n = 30
set.seed(333)
sample.idx <- sample(1:nrow(K_shape), size = n)
vars <- c("A2","A3","A4","A5","A6","A7","A8","B2","B3","B4","B5","B6","B7","B8")
pairs.panels(K_shape[sample.idx, vars], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species K')
1.3.2.8 N
n = 30
set.seed(333)
sample.idx <- sample(1:nrow(N_shape), size = n)
vars <- c("A2","A3","A4","A5","A6","A7","A8","B2","B3","B4","B5","B6","B7","B8")
pairs.panels(N_shape[sample.idx, vars], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species N')
1.3.2.9 O
pairs.panels(O_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species O')
1.3.2.10 P
pairs.panels(P_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species P')
1.3.2.11 Q3
pairs.panels(Q3_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species Q3')
1.3.2.12 Q4
pairs.panels(Q4_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species Q4')
1.3.2.13 Q5
n = 30
set.seed(333)
sample.idx <- sample(1:nrow(N_shape), size = n)
vars <- c("A2","A3","A4","A5","A6","A7","A8","B2","B3","B4","B5","B6","B7","B8")
pairs.panels(Q5_shape[sample.idx, vars], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species Q5')
1.3.2.14 R
pairs.panels(R_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species R')
1.3.2.15 S
n = 30
set.seed(333)
sample.idx <- sample(1:nrow(N_shape), size = n)
vars <- c("A2","A3","A4","A5","A6","A7","A8","B2","B3","B4","B5","B6","B7","B8")
pairs.panels(S_shape[sample.idx, vars], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species S')
1.3.2.16 weeksi
pairs.panels(weeksi_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species weeksi')
1.3.2.17 X2
#pairs.panels(X2_shape[2:15], smooth = F, ellipses = F,
# main = 'Pearson product moment correlation coefficient for species X2')1.3.2.18 X3
pairs.panels(X3_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species X3')
1.3.2.19 X4
pairs.panels(X4_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species X4')
1.3.2.20 X5
pairs.panels(X5_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species X5')
1.3.2.21 X8
pairs.panels(X8_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species X8')
1.3.2.22 X9
pairs.panels(X9_shape[2:15], smooth = F, ellipses = F,
main = 'Pearson product moment correlation coefficient for species X9')
2 Principal Component Analysis (PCA)
2.1 All
All.pca <- princomp(All[c(2:15)])
ggord(All.pca, All$Species2,size = 2.5, vec_ext=0.03, axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0) #+ xlim(-0.08,0.07) + ylim(-0.075, 0.03)
#ggord(All.pca, All$Species2,size = 2.5, vec_ext= 0.03, axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = FALSE, obslab = TRUE)
ggord(All.pca, All$Species2,size = 2.5, vec_ext=0.03, axes = c("1", "3"), hull = TRUE, ellipse=FALSE, arrow = 0) #+ xlim(-0.08,0.07) + ylim(-0.04, 0.045)
PC1, PC2, and PC3 explain 39.3%, 19.4%, and 10.5% of the variance in the dataset, respectively. Hence, a lot of variability is captured by further PCs. Also, carapace shapes of the closely related Q-species (Q1, Q3, Q4, Q5, weeksi) are highly variable. To further reveal morphological relationships between the Q-species and between the remaining species, we are carrying out two further PCAs for each of these groups.
2.2 All without Q species
All_ohne_Q.pca <- princomp(All_ohne_Q[c(2:15)])
ggord(All_ohne_Q.pca, All_ohne_Q$Species2,size = 2.5, vec_ext=0.03, axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0) #+ xlim(-0.08,0.07) + ylim(-0.075, 0.03)
2.3 All plus type specimens
shape_8.pca <- princomp(shape_8[c(2:15)])
ggord(shape_8.pca, shape_8$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0) #+ xlim(-0.075,0.055) + ylim(-0.04, 0.055)
ggord(shape_8.pca, shape_8$Species2,size = 2.5, vec_ext=0.03,axes = c("2", "3"), hull = TRUE, ellipse=FALSE, arrow = 0) #+ xlim(-0.04,0.055) + ylim(-0.04, 0.04)
2.4 Q_K_typica
Q_K_typica.pca <- princomp(Q_K_typica[c(2:15)])
ggord(Q_K_typica.pca, Q_K_typica$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
ggord(Q_K_typica.pca, Q_K_typica$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "3"), hull = TRUE, ellipse=FALSE, arrow = 0)
PC1, PC2, and PC3 explain 48.3%, 14.8%, and 9.1% of the variance in the dataset, respectively. The analysis of the closely related Q-species shows that each species occupies a large morphospace compared to other Ozestheria species. It also suggests that one of these species can be assigned to Ozestheria typica (see posterior probabilities and typicality values in section 4.6).
2.5 B_H2_O_S_X8_minor
B_H2_O_S_X8_minor.pca <- princomp(B_H2_O_S_X8_minor[c(2:15)])
ggord(B_H2_O_S_X8_minor.pca, B_H2_O_S_X8_minor$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
ggord(B_H2_O_S_X8_minor.pca, B_H2_O_S_X8_minor$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "3"), hull = TRUE, ellipse=FALSE, arrow = 0)
2.6 K_N_X5_mariae
K_N_X5_mariae.pca <- princomp(K_N_X5_mariae[c(2:15)])
ggord(K_N_X5_mariae.pca, K_N_X5_mariae$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
ggord(K_N_X5_mariae.pca, K_N_X5_mariae$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "3"), hull = TRUE, ellipse=FALSE, arrow = 0)
3 Linear Discriminant Analysis (LDA)
3.1 All
All.lda <- lda(Species2 ~ A2+A3+A4+A5+A6+A7+A8+B2+B3+B4+B5+B6+B7+B8, method = "moment", data = All, na.action = na.omit)
All.lda.values <- predict(All.lda)
ggord(All.lda, All$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
ggord(All.lda, All$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "3"), hull = TRUE, ellipse=FALSE, arrow = 0)
LD1 and LD2 explain 51.5% and 16.7% of the between-group variance, respectively.
3.2 All without Q
All_ohne_Q.lda <- lda(Species2 ~ A2+A3+A4+A5+A6+A7+A8+B2+B3+B4+B5+B6+B7+B8, method = "moment", data = All_ohne_Q, na.action = na.omit)
All_ohne_Q.lda.values <- predict(All_ohne_Q.lda)
ggord(All_ohne_Q.lda, All_ohne_Q$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
ggord(All_ohne_Q.lda, All_ohne_Q$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "3"), hull = TRUE, ellipse=FALSE, arrow = 0)
3.3 All plus type specimens
shape_8.lda <- lda(Species2 ~ A2+A3+A4+A5+A6+A7+A8+B2+B3+B4+B5+B6+B7+B8, method = "moment", data = shape_8, na.action = na.omit)
shape_8.lda.values <- predict(shape_8.lda)
ggord(shape_8.lda, shape_8$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
ggord(shape_8.lda, shape_8$Species2,size = 2.5, vec_ext=0.03,axes = c("1", "3"), hull = TRUE, ellipse=FALSE, arrow = 0)
3.4 Q_K_typica
Q_K_typica.lda <- lda(Species2 ~ A2+A3+A4+A5+A6+A7+A8+B2+B3+B4+B5+B6+B7+B8, method = "moment", data = Q_K_typica, na.action = na.omit)
Q_K_typica.lda.values <- predict(Q_K_typica.lda)
ggord(Q_K_typica.lda, Q_K_typica$Species2,size = 2.5, vec_ext=0.01,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
ggord(Q_K_typica.lda, Q_K_typica$Species2,size = 2.5, vec_ext=0.01,axes = c("1", "3"), hull = TRUE, ellipse=FALSE, arrow = 0)
3.5 B_H2_O_S_X8_minor
B_H2_O_S_X8_minor.lda <- lda(Species2 ~ A2+A3+A4+A5+A6+A7+A8+B2+B3+B4+B5+B6+B7+B8, method = "moment", data = B_H2_O_S_X8_minor, na.action = na.omit)
B_H2_O_S_X8_minor.lda.values <- predict(B_H2_O_S_X8_minor.lda)
ggord(B_H2_O_S_X8_minor.lda, B_H2_O_S_X8_minor$Species2,size = 2.5, vec_ext=0.01,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
3.6 K_N_X5_mariae
K_N_X5_mariae.lda <- lda(Species2 ~ A2+A3+A4+A5+A6+A7+A8+B2+B3+B4+B5+B6+B7+B8, method = "moment", data = K_N_X5_mariae, na.action = na.omit)
K_N_X5_mariae.lda.values <- predict(K_N_X5_mariae.lda)
ggord(K_N_X5_mariae.lda, K_N_X5_mariae$Species2,size = 2.5, vec_ext=0.01,axes = c("1", "2"), hull = TRUE, ellipse=FALSE, arrow = 0)
4 Classification of type specimens
Following Boedeker and Kearns (2019) Linear Discriminant Analysis for prediction of group membership
options(width = 120)
n.class <- 22 #number of species
p <- 14 #number of predictors
N <- 475
All_singletons.lda <- lda(Species2 ~ A2+A3+A4+A5+A6+A7+A8+B2+B3+B4+B5+B6+B7+B8, method = "moment", data = All_singletons, na.action = na.omit)
### means of Fourier coefficients (predictors) within species (class) ###
mean.A <- c(mean(A_shape$A2), mean(A_shape$A3),mean(A_shape$A4),mean(A_shape$A5),mean(A_shape$A6),mean(A_shape$A7),mean(A_shape$A8), mean(A_shape$B2), mean(A_shape$B3), mean(A_shape$B4), mean(A_shape$B5), mean(A_shape$B6), mean(A_shape$B7), mean(A_shape$B8))
mean.B <- c(mean(B_shape$A2), mean(B_shape$A3),mean(B_shape$A4),mean(B_shape$A5),mean(B_shape$A6),mean(B_shape$A7),mean(B_shape$A8), mean(B_shape$B2), mean(B_shape$B3), mean(B_shape$B4), mean(B_shape$B5), mean(B_shape$B6), mean(B_shape$B7), mean(B_shape$B8))
mean.E <- c(mean(E_shape$A2), mean(E_shape$A3),mean(E_shape$A4),mean(E_shape$A5),mean(E_shape$A6),mean(E_shape$A7),mean(E_shape$A8), mean(E_shape$B2), mean(E_shape$B3), mean(E_shape$B4), mean(E_shape$B5), mean(E_shape$B6), mean(E_shape$B7), mean(E_shape$B8))
mean.H1 <- c(mean(H1_shape$A2), mean(H1_shape$A3),mean(H1_shape$A4),mean(H1_shape$A5),mean(H1_shape$A6),mean(H1_shape$A7),mean(H1_shape$A8), mean(H1_shape$B2), mean(H1_shape$B3), mean(H1_shape$B4), mean(H1_shape$B5), mean(H1_shape$B6), mean(H1_shape$B7), mean(H1_shape$B8))
mean.H2 <- c(mean(H2_shape$A2), mean(H2_shape$A3),mean(H2_shape$A4),mean(H2_shape$A5),mean(H2_shape$A6),mean(H2_shape$A7),mean(H2_shape$A8), mean(H2_shape$B2), mean(H2_shape$B3), mean(H2_shape$B4), mean(H2_shape$B5), mean(H2_shape$B6), mean(H2_shape$B7), mean(H2_shape$B8))
mean.I <- c(mean(I_shape$A2), mean(I_shape$A3),mean(I_shape$A4),mean(I_shape$A5),mean(I_shape$A6),mean(I_shape$A7),mean(I_shape$A8), mean(I_shape$B2), mean(I_shape$B3), mean(I_shape$B4), mean(I_shape$B5), mean(I_shape$B6), mean(I_shape$B7), mean(I_shape$B8))
mean.K <- c(mean(K_shape$A2), mean(K_shape$A3),mean(K_shape$A4),mean(K_shape$A5),mean(K_shape$A6),mean(K_shape$A7),mean(K_shape$A8), mean(K_shape$B2), mean(K_shape$B3), mean(K_shape$B4), mean(K_shape$B5), mean(K_shape$B6), mean(K_shape$B7), mean(K_shape$B8))
mean.N <- c(mean(N_shape$A2), mean(N_shape$A3),mean(N_shape$A4),mean(N_shape$A5),mean(N_shape$A6),mean(N_shape$A7),mean(N_shape$A8), mean(N_shape$B2), mean(N_shape$B3), mean(N_shape$B4), mean(N_shape$B5), mean(N_shape$B6), mean(N_shape$B7), mean(N_shape$B8))
mean.O <- c(mean(O_shape$A2), mean(O_shape$A3),mean(O_shape$A4),mean(O_shape$A5),mean(O_shape$A6),mean(O_shape$A7),mean(O_shape$A8), mean(O_shape$B2), mean(O_shape$B3), mean(O_shape$B4), mean(O_shape$B5), mean(O_shape$B6), mean(O_shape$B7), mean(O_shape$B8))
mean.P <- c(mean(P_shape$A2), mean(P_shape$A3),mean(P_shape$A4),mean(P_shape$A5),mean(P_shape$A6),mean(P_shape$A7),mean(P_shape$A8), mean(P_shape$B2), mean(P_shape$B3), mean(P_shape$B4), mean(P_shape$B5), mean(P_shape$B6), mean(P_shape$B7), mean(P_shape$B8))
mean.Q3 <- c(mean(Q3_shape$A2), mean(Q3_shape$A3),mean(Q3_shape$A4),mean(Q3_shape$A5),mean(Q3_shape$A6),mean(Q3_shape$A7),mean(Q3_shape$A8), mean(Q3_shape$B2), mean(Q3_shape$B3), mean(Q3_shape$B4), mean(Q3_shape$B5), mean(Q3_shape$B6), mean(Q3_shape$B7), mean(Q3_shape$B8))
mean.Q4 <- c(mean(Q4_shape$A2), mean(Q4_shape$A3),mean(Q4_shape$A4),mean(Q4_shape$A5),mean(Q4_shape$A6),mean(Q4_shape$A7),mean(Q4_shape$A8), mean(Q4_shape$B2), mean(Q4_shape$B3), mean(Q4_shape$B4), mean(Q4_shape$B5), mean(Q4_shape$B6), mean(Q4_shape$B7), mean(Q4_shape$B8))
mean.Q5 <- c(mean(Q5_shape$A2), mean(Q5_shape$A3),mean(Q5_shape$A4),mean(Q5_shape$A5),mean(Q5_shape$A6),mean(Q5_shape$A7),mean(Q5_shape$A8), mean(Q5_shape$B2), mean(Q5_shape$B3), mean(Q5_shape$B4), mean(Q5_shape$B5), mean(Q5_shape$B6), mean(Q5_shape$B7), mean(Q5_shape$B8))
mean.R <- c(mean(R_shape$A2), mean(R_shape$A3),mean(R_shape$A4),mean(R_shape$A5),mean(R_shape$A6),mean(R_shape$A7),mean(R_shape$A8), mean(R_shape$B2), mean(R_shape$B3), mean(R_shape$B4), mean(R_shape$B5), mean(R_shape$B6), mean(R_shape$B7), mean(R_shape$B8))
mean.S <- c(mean(S_shape$A2), mean(S_shape$A3),mean(S_shape$A4),mean(S_shape$A5),mean(S_shape$A6),mean(S_shape$A7),mean(S_shape$A8), mean(S_shape$B2), mean(S_shape$B3), mean(S_shape$B4), mean(S_shape$B5), mean(S_shape$B6), mean(S_shape$B7), mean(S_shape$B8))
mean.weeksi <- c(mean(weeksi_shape$A2), mean(weeksi_shape$A3),mean(weeksi_shape$A4),mean(weeksi_shape$A5),mean(weeksi_shape$A6),mean(weeksi_shape$A7),mean(weeksi_shape$A8), mean(weeksi_shape$B2), mean(weeksi_shape$B3), mean(weeksi_shape$B4), mean(weeksi_shape$B5), mean(weeksi_shape$B6), mean(weeksi_shape$B7), mean(weeksi_shape$B8))
mean.X2 <- c(mean(X2_shape$A2), mean(X2_shape$A3),mean(X2_shape$A4),mean(X2_shape$A5),mean(X2_shape$A6),mean(X2_shape$A7),mean(X2_shape$A8), mean(X2_shape$B2), mean(X2_shape$B3), mean(X2_shape$B4), mean(X2_shape$B5), mean(X2_shape$B6), mean(X2_shape$B7), mean(X2_shape$B8))
mean.X3 <- c(mean(X3_shape$A2), mean(X3_shape$A3),mean(X3_shape$A4),mean(X3_shape$A5),mean(X3_shape$A6),mean(X3_shape$A7),mean(X3_shape$A8), mean(X3_shape$B2), mean(X3_shape$B3), mean(X3_shape$B4), mean(X3_shape$B5), mean(X3_shape$B6), mean(X3_shape$B7), mean(X3_shape$B8))
mean.X4 <- c(mean(X4_shape$A2), mean(X4_shape$A3),mean(X4_shape$A4),mean(X4_shape$A5),mean(X4_shape$A6),mean(X4_shape$A7),mean(X4_shape$A8), mean(X4_shape$B2), mean(X4_shape$B3), mean(X4_shape$B4), mean(X4_shape$B5), mean(X4_shape$B6), mean(X4_shape$B7), mean(X4_shape$B8))
mean.X5 <- c(mean(X5_shape$A2), mean(X5_shape$A3),mean(X5_shape$A4),mean(X5_shape$A5),mean(X5_shape$A6),mean(X5_shape$A7),mean(X5_shape$A8), mean(X5_shape$B2), mean(X5_shape$B3), mean(X5_shape$B4), mean(X5_shape$B5), mean(X5_shape$B6), mean(X5_shape$B7), mean(X5_shape$B8))
mean.X8 <- c(mean(X8_shape$A2), mean(X8_shape$A3),mean(X8_shape$A4),mean(X8_shape$A5),mean(X8_shape$A6),mean(X8_shape$A7),mean(X8_shape$A8), mean(X8_shape$B2), mean(X8_shape$B3), mean(X8_shape$B4), mean(X8_shape$B5), mean(X8_shape$B6), mean(X8_shape$B7), mean(X8_shape$B8))
mean.X9 <- c(mean(X9_shape$A2), mean(X9_shape$A3),mean(X9_shape$A4),mean(X9_shape$A5),mean(X9_shape$A6),mean(X9_shape$A7),mean(X9_shape$A8), mean(X9_shape$B2), mean(X9_shape$B3), mean(X9_shape$B4), mean(X9_shape$B5), mean(X9_shape$B6), mean(X9_shape$B7), mean(X9_shape$B8))
mean.A <- as.matrix(mean.A)
mean.B <- as.matrix(mean.B)
mean.E <- as.matrix(mean.E)
mean.H1 <- as.matrix(mean.H1)
mean.H2 <- as.matrix(mean.H2)
mean.I <- as.matrix(mean.I)
mean.K <- as.matrix(mean.K)
mean.N <- as.matrix(mean.N)
mean.O <- as.matrix(mean.O)
mean.P <- as.matrix(mean.P)
mean.Q3 <- as.matrix(mean.Q3)
mean.Q4 <- as.matrix(mean.Q4)
mean.Q5 <- as.matrix(mean.Q5)
mean.R <- as.matrix(mean.R)
mean.S <- as.matrix(mean.S)
mean.weeksi <- as.matrix(mean.weeksi)
mean.X2 <- as.matrix(mean.X2)
mean.X3 <- as.matrix(mean.X3)
mean.X4 <- as.matrix(mean.X4)
mean.X5 <- as.matrix(mean.X5)
mean.X8 <- as.matrix(mean.X8)
mean.X9 <- as.matrix(mean.X9)
### Variance-covariance matrix ###
cov.A <- cov(A_shape[2:15])
cov.B <- cov(B_shape[2:15])
cov.E <- cov(E_shape[2:15])
cov.H1 <- cov(H1_shape[2:15])
cov.H2 <- cov(H2_shape[2:15])
cov.I <- cov(I_shape[2:15])
cov.K <- cov(K_shape[2:15])
cov.N <- cov(N_shape[2:15])
cov.O <- cov(O_shape[2:15])
cov.P <- cov(P_shape[2:15])
cov.Q3 <- cov(Q3_shape[2:15])
cov.Q4 <- cov(Q4_shape[2:15])
cov.Q5 <- cov(Q5_shape[2:15])
cov.R <- cov(R_shape[2:15])
cov.S <- cov(S_shape[2:15])
cov.weeksi <- cov(weeksi_shape[2:15])
cov.X2 <- cov(X2_shape[2:15])
cov.X3 <- cov(X3_shape[2:15])
cov.X4 <- cov(X4_shape[2:15])
cov.X5 <- cov(X5_shape[2:15])
cov.X8 <- cov(X8_shape[2:15])
cov.X9 <- cov(X9_shape[2:15])
### sample size ###
n.A <- dim(A_shape) [1]
n.B <- dim(B_shape) [1]
n.E <- dim(E_shape) [1]
n.H1 <- dim(H1_shape) [1]
n.H2 <- dim(H2_shape) [1]
n.I <- dim(I_shape) [1]
n.K <- dim(K_shape) [1]
n.N <- dim(N_shape) [1]
n.O <- dim(O_shape) [1]
n.P <- dim(P_shape) [1]
n.Q3 <- dim(Q3_shape) [1]
n.Q4 <- dim(Q4_shape) [1]
n.Q5 <- dim(Q5_shape) [1]
n.R <- dim(R_shape) [1]
n.S <- dim(S_shape) [1]
n.weeksi <- dim(weeksi_shape) [1]
n.X2 <- dim(X2_shape) [1]
n.X3 <- dim(X3_shape) [1]
n.X4 <- dim(X4_shape) [1]
n.X5 <- dim(X5_shape) [1]
n.X8 <- dim(X8_shape) [1]
n.X9 <- dim(X9_shape) [1]
cov.df <- n.A+n.B+n.E+n.H1+n.H2+n.I+n.K+n.N+n.O+n.P+n.Q3+n.Q4+n.Q5+n.R+n.S+n.weeksi+n.X2+n.X3+n.X4+n.X5+n.X8+n.X9-n.class
cov.d <-
((n.A-1)/cov.df)*cov.A+
((n.B-1)/cov.df)*cov.B+
((n.E-1)/cov.df)*cov.E+
((n.H1-1)/cov.df)*cov.H1+
((n.H2-1)/cov.df)*cov.H2+
((n.I-1)/cov.df)*cov.I+
((n.K-1)/cov.df)*cov.K+
((n.N-1)/cov.df)*cov.N+
((n.O-1)/cov.df)*cov.O+
((n.P-1)/cov.df)*cov.P+
((n.Q3-1)/cov.df)*cov.Q3+
((n.Q4-1)/cov.df)*cov.Q4+
((n.Q5-1)/cov.df)*cov.Q5+
((n.R-1)/cov.df)*cov.R+
((n.S-1)/cov.df)*cov.S+
((n.weeksi-1)/cov.df)*cov.weeksi+
((n.X2-1)/cov.df)*cov.X2+
((n.X3-1)/cov.df)*cov.X3+
((n.X4-1)/cov.df)*cov.X4+
((n.X5-1)/cov.df)*cov.X5+
((n.X8-1)/cov.df)*cov.X8+
((n.X9-1)/cov.df)*cov.X9
### determinant
d <- det(cov.d)
### Coefficients of Linear classification function (loadings)
cj.A <- solve(cov.d)%*%mean.A
cj.B <- solve(cov.d)%*%mean.B
cj.E <- solve(cov.d)%*%mean.E
cj.H1 <- solve(cov.d)%*%mean.H1
cj.H2 <- solve(cov.d)%*%mean.H2
cj.I <- solve(cov.d)%*%mean.I
cj.K <- solve(cov.d)%*%mean.K
cj.N <- solve(cov.d)%*%mean.N
cj.O <- solve(cov.d)%*%mean.O
cj.P <- solve(cov.d)%*%mean.P
cj.Q3 <- solve(cov.d)%*%mean.Q3
cj.Q4 <- solve(cov.d)%*%mean.Q4
cj.Q5 <- solve(cov.d)%*%mean.Q5
cj.R <- solve(cov.d)%*%mean.R
cj.S <- solve(cov.d)%*%mean.S
cj.weeksi <- solve(cov.d)%*%mean.weeksi
cj.X2 <- solve(cov.d)%*%mean.X2
cj.X3 <- solve(cov.d)%*%mean.X3
cj.X4 <- solve(cov.d)%*%mean.X4
cj.X5 <- solve(cov.d)%*%mean.X5
cj.X8 <- solve(cov.d)%*%mean.X8
cj.X9 <- solve(cov.d)%*%mean.X9
### Intercepts
cj0.A <- -.5*t(cj.A)%*%mean.A
cj0.B <- -.5*t(cj.B)%*%mean.B
cj0.E <- -.5*t(cj.E)%*%mean.E
cj0.H1 <- -.5*t(cj.H1)%*%mean.H1
cj0.H2 <- -.5*t(cj.H2)%*%mean.H2
cj0.I <- -.5*t(cj.I)%*%mean.I
cj0.K <- -.5*t(cj.K)%*%mean.K
cj0.N <- -.5*t(cj.N)%*%mean.N
cj0.O <- -.5*t(cj.O)%*%mean.O
cj0.P <- -.5*t(cj.P)%*%mean.P
cj0.Q3 <- -.5*t(cj.Q3)%*%mean.Q3
cj0.Q4 <- -.5*t(cj.Q4)%*%mean.Q4
cj0.Q5 <- -.5*t(cj.Q5)%*%mean.Q5
cj0.R <- -.5*t(cj.R)%*%mean.R
cj0.S <- -.5*t(cj.S)%*%mean.S
cj0.weeksi <- -.5*t(cj.weeksi)%*%mean.weeksi
cj0.X2 <- -.5*t(cj.X2)%*%mean.X2
cj0.X3 <- -.5*t(cj.X3)%*%mean.X3
cj0.X4 <- -.5*t(cj.X4)%*%mean.X4
cj0.X5 <- -.5*t(cj.X5)%*%mean.X5
cj0.X8 <- -.5*t(cj.X8)%*%mean.X8
cj0.X9 <- -.5*t(cj.X9)%*%mean.X94.1 Typicality all specimens
### Typicality probabilities ###
typicality <- matrix(NA, N, n.class)
colnames(typicality) <- c("typA","typB","typE","typH1","typH2","typI","typK","typN","typO","typP","typQ3","typQ4","typQ5","typR","typS","typweeksi","typX2","typX3","typX4","typX5","typX8","typX9")
for(q in 1:N) {
case <- matrix(NA,22,1)
case <- c(All_singletons[q,2],All_singletons[q,3],All_singletons[q,4],All_singletons[q,5],All_singletons[q,6],All_singletons[q,7],All_singletons[q,8],All_singletons[q,9],All_singletons[q,10],All_singletons[q,11],All_singletons[q,12],All_singletons[q,13],All_singletons[q,14],All_singletons[q,15])
d2.A <- (t(case-mean.A))%*%solve(cov.d)%*%(case-mean.A)
typicality[q,1] <- pchisq(d2.A, df = 14, lower.tail = FALSE)
d2.B <- (t(case-mean.B))%*%solve(cov.d)%*%(case-mean.B)
typicality[q,2] <- pchisq(d2.B, df = 14, lower.tail = FALSE)
d2.E <- (t(case-mean.E))%*%solve(cov.d)%*%(case-mean.E)
typicality[q,3] <- pchisq(d2.E, df = 14, lower.tail = FALSE)
d2.H1 <- (t(case-mean.H1))%*%solve(cov.d)%*%(case-mean.H1)
typicality[q,4] <- pchisq(d2.H1, df = 14, lower.tail = FALSE)
d2.H2 <- (t(case-mean.H2))%*%solve(cov.d)%*%(case-mean.H2)
typicality[q,5] <- pchisq(d2.H2, df = 14, lower.tail = FALSE)
d2.I <- (t(case-mean.I))%*%solve(cov.d)%*%(case-mean.I)
typicality[q,6] <- pchisq(d2.I, df = 14, lower.tail = FALSE)
d2.K <- (t(case-mean.K))%*%solve(cov.d)%*%(case-mean.K)
typicality[q,7] <- pchisq(d2.K, df = 14, lower.tail = FALSE)
d2.N <- (t(case-mean.N))%*%solve(cov.d)%*%(case-mean.N)
typicality[q,8] <- pchisq(d2.N, df = 14, lower.tail = FALSE)
d2.O <- (t(case-mean.O))%*%solve(cov.d)%*%(case-mean.O)
typicality[q,9] <- pchisq(d2.O, df = 14, lower.tail = FALSE)
d2.P <- (t(case-mean.P))%*%solve(cov.d)%*%(case-mean.P)
typicality[q,10] <- pchisq(d2.P, df = 14, lower.tail = FALSE)
d2.Q3 <- (t(case-mean.Q3))%*%solve(cov.d)%*%(case-mean.Q3)
typicality[q,11] <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q4 <- (t(case-mean.Q4))%*%solve(cov.d)%*%(case-mean.Q4)
typicality[q,12] <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q5 <- (t(case-mean.Q5))%*%solve(cov.d)%*%(case-mean.Q5)
typicality[q,13] <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.R <- (t(case-mean.R))%*%solve(cov.d)%*%(case-mean.R)
typicality[q,14] <- pchisq(d2.R, df = 14, lower.tail = FALSE)
d2.S <- (t(case-mean.S))%*%solve(cov.d)%*%(case-mean.S)
typicality[q,15] <- pchisq(d2.S, df = 14, lower.tail = FALSE)
d2.weeksi <- (t(case-mean.weeksi))%*%solve(cov.d)%*%(case-mean.weeksi)
typicality[q,16] <- pchisq(d2.weeksi, df = 14, lower.tail = FALSE)
d2.X2 <- (t(case-mean.X2))%*%solve(cov.d)%*%(case-mean.X2)
typicality[q,17] <- pchisq(d2.X2, df = 14, lower.tail = FALSE)
d2.X3 <- (t(case-mean.X3))%*%solve(cov.d)%*%(case-mean.X3)
typicality[q,18] <- pchisq(d2.X3, df = 14, lower.tail = FALSE)
d2.X4 <- (t(case-mean.X4))%*%solve(cov.d)%*%(case-mean.X4)
typicality[q,19] <- pchisq(d2.X4, df = 14, lower.tail = FALSE)
d2.X5 <- (t(case-mean.X5))%*%solve(cov.d)%*%(case-mean.X5)
typicality[q,20] <- pchisq(d2.X5, df = 14, lower.tail = FALSE)
d2.X8 <- (t(case-mean.X8))%*%solve(cov.d)%*%(case-mean.X8)
typicality[q,21] <- pchisq(d2.X8, df = 14, lower.tail = FALSE)
d2.X9 <- (t(case-mean.X9))%*%solve(cov.d)%*%(case-mean.X9)
typicality[q,22] <- pchisq(d2.X9, df = 14, lower.tail = FALSE)
}
typicality <- round(typicality, digits=2)
typicality <- cbind(All_singletons[ ,c(1,17:18)], typicality)
typicality## Collection Species2 Sex typA typB typE typH1 typH2 typI typK typN typO typP typQ3 typQ4 typQ5 typR typS
## 69 91437 A F 0.48 0.19 0.00 0.00 0.01 0.00 0.00 0.00 0.02 0.00 0.05 0.05 0.05 0.00 0.01
## 70 91439 A F 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.03 0.00 0.01
## 71 91440 A M 0.66 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.11 0.11 0.00 0.03
## 72 91441 A F 0.59 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.00 0.00
## 73 91442 A F 0.65 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.15 0.15 0.03 0.01
## 74 91443 A F_eggs 0.60 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.19 0.19 0.19 0.00 0.10
## 75 91444 A M 0.65 0.83 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.25 0.25 0.25 0.00 0.13
## 76 91445 A F_eggs 0.20 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.02
## 77 91446 A M 0.81 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.10 0.00 0.06
## 78 91447 A M 0.65 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.11 0.11 0.11 0.00 0.14
## 79 91448 A M 0.97 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.63 0.63 0.63 0.04 0.18
## 80 91449 A M 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.00 0.00
## 81 91450 A M 0.87 0.51 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.81 0.81 0.81 0.24 0.18
## 82 91451 A F_eggs 0.94 0.09 0.00 0.00 0.02 0.01 0.00 0.00 0.02 0.03 0.48 0.48 0.48 0.06 0.35
## 83 91452 A M 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.07 0.07 0.00 0.02
## 84 91453 A M 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.03 0.00 0.01
## 85 91454 A M 0.62 0.18 0.00 0.01 0.00 0.00 0.00 0.00 0.04 0.00 0.76 0.76 0.76 0.08 0.35
## 86 91455 A M 0.66 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.36 0.36 0.36 0.02 0.10
## 87 91456 A M 0.59 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00
## 88 91457 A M 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 89 91458 A M 0.65 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00
## 90 91459 A M 0.91 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.00 0.00
## 91 91460 A M 0.84 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.18 0.18 0.01 0.00
## 92 91462 A F 0.75 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.10 0.00 0.01
## 93 91463 A M 0.53 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.09 0.09 0.00 0.00
## 94 91464 A M 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 95 91469 A F_eggs 0.02 0.04 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.12 0.12 0.12 0.09 0.01
## 96 91470 A F 0.69 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00
## 97 91471 A F 0.40 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 2 80862 B M 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.03 0.00 0.00
## 98 91477 B M 0.00 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00
## 99 91478 B M 0.03 0.54 0.00 0.00 0.00 0.01 0.00 0.00 0.02 0.00 0.22 0.22 0.22 0.05 0.06
## 100 91479 B F 0.19 0.86 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.08 0.08 0.08 0.00 0.01
## 101 91480 B F 0.03 0.66 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## 102 91481 B M 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 103 91482 B F 0.06 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.02 0.01
## 104 91486 B F_eggs 0.00 0.19 0.19 0.00 0.00 0.04 0.01 0.00 0.09 0.02 0.02 0.02 0.02 0.00 0.04
## 105 91487 B M 0.06 0.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.10 0.03 0.00
## 106 91489 B F 0.06 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 107 91490 B F 0.12 0.96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.08 0.08 0.05 0.00
## 108 91492 B F 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 109 91493 B M 0.02 0.91 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.06 0.06 0.06 0.00 0.01
## 110 91494 B M 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 111 91495 B M 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 112 91496 B M 0.01 0.72 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.13 0.13 0.13 0.06 0.03
## 113 91497 B F_eggs 0.00 0.84 0.02 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.06 0.06 0.06 0.03 0.02
## 114 91498 B F_eggs 0.01 0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.04 0.04 0.04 0.01 0.02
## 115 91500 B M 0.00 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.03 0.03 0.00
## 116 91501 B F 0.03 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.06 0.06 0.03 0.00
## 117 91502 B F_eggs 0.65 0.85 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.25 0.25 0.25 0.07 0.05
## 118 91503 B M 0.17 0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00
## 119 91504 B M 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.05 0.01 0.00
## 120 91505 B M 0.08 0.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.01 0.00
## 121 91506 B M 0.00 0.74 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.03 0.03 0.03 0.01 0.01
## 122 91507 B F 0.00 0.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 123 91508 B M 0.01 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.05 0.05 0.05 0.02 0.00
## 124 91512 B M 0.30 0.82 0.03 0.00 0.00 0.00 0.00 0.00 0.01 0.14 0.33 0.33 0.33 0.02 0.12
## 125 91513 B M 0.60 0.97 0.01 0.02 0.07 0.02 0.00 0.00 0.11 0.09 0.55 0.55 0.55 0.03 0.29
## 126 91514 B F_eggs 0.35 0.84 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.16 0.16 0.16 0.01 0.03
## 127 91515 B M 0.14 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.10 0.00 0.00
## 58 91420 E M 0.00 0.00 0.86 0.00 0.00 0.01 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00
## 59 91421 E F_eggs 0.00 0.00 0.91 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 60 91422 E F_eggs 0.00 0.00 0.83 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 61 91423 E F_eggs 0.00 0.00 0.62 0.00 0.00 0.04 0.00 0.00 0.00 0.10 0.01 0.01 0.01 0.00 0.01
## 62 91424 E F 0.00 0.01 0.99 0.00 0.00 0.11 0.00 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.01
## 63 91425 E F 0.00 0.00 0.95 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 64 91426 E M 0.00 0.00 0.83 0.01 0.00 0.33 0.25 0.01 0.00 0.29 0.00 0.00 0.00 0.01 0.02
## 65 91427 E M 0.00 0.08 0.99 0.00 0.00 0.27 0.05 0.00 0.00 0.07 0.01 0.01 0.01 0.01 0.02
## 66 91428 E F 0.00 0.00 0.52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 67 91429 E F 0.00 0.01 0.73 0.00 0.00 0.02 0.10 0.00 0.00 0.11 0.01 0.01 0.01 0.01 0.01
## 68 91430 E M 0.00 0.01 0.31 0.00 0.00 0.04 0.20 0.00 0.01 0.03 0.01 0.01 0.01 0.01 0.01
## 128 91532 H1 F 0.00 0.00 0.00 0.69 0.23 0.01 0.00 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## 129 91533 H1 F_eggs 0.00 0.00 0.00 0.51 0.06 0.00 0.00 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## 135 91544 H1 M 0.00 0.03 0.00 0.52 0.12 0.19 0.31 0.03 0.56 0.21 0.08 0.08 0.08 0.00 0.55
## 383 26637a H1 F_eggs 0.00 0.09 0.24 0.84 0.15 0.97 0.10 0.23 0.27 0.36 0.09 0.09 0.09 0.02 0.62
## 384 26637b H1 F_eggs 0.00 0.00 0.00 0.42 0.05 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01
## 385 26637c H1 M 0.00 0.00 0.00 0.76 0.04 0.20 0.14 0.11 0.53 0.17 0.08 0.08 0.08 0.01 0.72
## 386 26637d H1 F_eggs 0.00 0.00 0.00 0.68 0.19 0.04 0.00 0.02 0.04 0.00 0.00 0.00 0.00 0.00 0.01
## 387 26637e H1 M 0.00 0.01 0.00 0.77 0.05 0.14 0.04 0.02 0.18 0.10 0.05 0.05 0.05 0.00 0.47
## 388 26637f H1 F_eggs 0.02 0.04 0.00 0.86 0.16 0.16 0.00 0.01 0.15 0.04 0.07 0.07 0.07 0.00 0.42
## 389 26637g H1 F_eggs 0.00 0.00 0.00 0.90 0.04 0.34 0.00 0.78 0.02 0.08 0.00 0.00 0.00 0.00 0.05
## 390 26637h H1 M 0.00 0.00 0.00 0.28 0.03 0.01 0.02 0.00 0.46 0.01 0.05 0.05 0.05 0.00 0.28
## 391 26637i H1 F_eggs 0.00 0.00 0.00 0.98 0.17 0.30 0.00 0.28 0.09 0.01 0.00 0.00 0.00 0.00 0.15
## 392 26637j H1 F_eggs 0.00 0.00 0.00 0.86 0.03 0.36 0.00 0.48 0.02 0.00 0.00 0.00 0.00 0.00 0.01
## 393 26639a H1 M 0.00 0.00 0.00 0.99 0.26 0.71 0.01 0.68 0.24 0.08 0.00 0.00 0.00 0.00 0.37
## 394 26639b H1 M 0.00 0.00 0.00 0.97 0.05 0.41 0.01 0.14 0.18 0.09 0.00 0.00 0.00 0.00 0.23
## 395 26639c H1 F_eggs 0.00 0.00 0.00 0.54 0.13 0.60 0.03 0.11 0.08 0.04 0.00 0.00 0.00 0.00 0.07
## 396 26641a H1 F_eggs 0.00 0.00 0.00 0.98 0.18 0.50 0.09 0.52 0.39 0.08 0.00 0.00 0.00 0.00 0.22
## 397 26641b H1 F_eggs 0.00 0.00 0.00 0.70 0.03 0.09 0.00 0.19 0.07 0.03 0.00 0.00 0.00 0.00 0.04
## 398 26641c H1 F 0.00 0.00 0.00 0.59 0.08 0.04 0.00 0.21 0.02 0.01 0.00 0.00 0.00 0.00 0.01
## 130 91539 H2 F_eggs 0.00 0.00 0.00 0.15 0.84 0.04 0.01 0.01 0.37 0.00 0.00 0.00 0.00 0.00 0.08
## 131 91540 H2 F_eggs 0.01 0.03 0.00 0.23 0.98 0.02 0.00 0.00 0.90 0.00 0.01 0.01 0.01 0.00 0.22
## 132 91541 H2 F_eggs 0.00 0.00 0.00 0.62 0.94 0.10 0.01 0.08 0.29 0.01 0.00 0.00 0.00 0.00 0.07
## 133 91542 H2 F_eggs 0.00 0.00 0.00 0.07 0.68 0.02 0.00 0.00 0.10 0.01 0.00 0.00 0.00 0.00 0.04
## 134 91543 H2 F_eggs 0.00 0.00 0.00 0.01 0.67 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00
## 136 91546 I M 0.00 0.00 0.04 0.05 0.02 0.78 0.13 0.04 0.02 0.02 0.00 0.00 0.00 0.00 0.05
## 137 91547 I M 0.00 0.00 0.00 0.83 0.06 0.92 0.30 0.77 0.16 0.49 0.01 0.01 0.01 0.00 0.45
## 138 91548 I F 0.00 0.00 0.47 0.05 0.01 0.84 0.02 0.12 0.01 0.07 0.00 0.00 0.00 0.00 0.04
## 139 91549 I F 0.00 0.00 0.01 0.29 0.01 0.81 0.62 0.15 0.19 0.71 0.05 0.05 0.05 0.01 0.63
## 9 82539 K F 0.00 0.00 0.47 0.00 0.00 0.03 0.25 0.00 0.00 0.05 0.01 0.01 0.01 0.01 0.01
## 10 82540 K M 0.00 0.00 0.00 0.00 0.00 0.03 0.36 0.00 0.02 0.18 0.07 0.07 0.07 0.02 0.14
## 140 91550 K M 0.00 0.00 0.00 0.01 0.00 0.24 0.87 0.02 0.00 0.11 0.00 0.00 0.00 0.00 0.04
## 141 91551 K M 0.00 0.00 0.01 0.15 0.00 0.83 0.99 0.50 0.02 0.64 0.01 0.01 0.01 0.00 0.21
## 142 91552 K M 0.00 0.00 0.00 0.18 0.00 0.22 0.19 0.40 0.00 0.08 0.00 0.00 0.00 0.00 0.04
## 143 91553 K M 0.00 0.00 0.00 0.01 0.00 0.03 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 144 91554 K M 0.00 0.00 0.01 0.12 0.01 0.28 0.79 0.16 0.10 0.43 0.11 0.11 0.11 0.00 0.39
## 145 91555 K M 0.00 0.01 0.00 0.00 0.00 0.07 0.28 0.00 0.01 0.04 0.10 0.10 0.10 0.05 0.13
## 146 91556 K M 0.00 0.00 0.00 0.57 0.07 0.83 0.99 0.40 0.45 0.50 0.11 0.11 0.11 0.01 0.72
## 147 91557 K F_eggs 0.00 0.00 0.13 0.00 0.00 0.07 0.41 0.00 0.00 0.14 0.01 0.01 0.01 0.01 0.03
## 148 91558 K F_eggs 0.00 0.00 0.00 0.00 0.00 0.21 0.39 0.15 0.00 0.09 0.00 0.00 0.00 0.00 0.01
## 149 91559 K M 0.00 0.00 0.00 0.00 0.00 0.02 0.77 0.01 0.01 0.07 0.00 0.00 0.00 0.00 0.02
## 150 91560 K M 0.00 0.04 0.01 0.29 0.01 0.74 0.84 0.03 0.37 0.43 0.40 0.40 0.40 0.27 0.80
## 151 91561 K F_eggs 0.00 0.00 0.00 0.00 0.00 0.04 0.54 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00
## 152 91562 K F_eggs 0.00 0.00 0.00 0.00 0.00 0.01 0.44 0.00 0.02 0.02 0.03 0.03 0.03 0.01 0.03
## 153 91563 K F 0.00 0.00 0.04 0.00 0.00 0.20 0.92 0.02 0.00 0.03 0.00 0.00 0.00 0.01 0.02
## 154 91564 K F 0.00 0.00 0.00 0.00 0.00 0.04 0.75 0.00 0.00 0.03 0.00 0.00 0.00 0.01 0.01
## 155 91565 K F_eggs 0.00 0.00 0.00 0.00 0.00 0.01 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 156 91566 K F_eggs 0.00 0.00 0.00 0.00 0.00 0.17 0.90 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01
## 157 91567 K F 0.00 0.00 0.00 0.00 0.00 0.03 0.66 0.00 0.00 0.03 0.00 0.00 0.00 0.04 0.00
## 158 91568 K F_eggs 0.00 0.00 0.01 0.00 0.00 0.05 0.35 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00
## 159 91569 K F 0.00 0.00 0.28 0.03 0.00 0.84 0.99 0.11 0.05 0.38 0.03 0.03 0.03 0.01 0.16
## 160 91570 K F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00
## 161 91571 K M 0.00 0.00 0.00 0.00 0.00 0.12 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 162 91572 K F_eggs 0.00 0.00 0.00 0.00 0.00 0.01 0.61 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00
## 163 91573 K F 0.00 0.00 0.02 0.04 0.00 0.47 0.69 0.01 0.01 0.02 0.01 0.01 0.01 0.00 0.03
## 164 91574 K F_eggs 0.00 0.00 0.00 0.01 0.01 0.06 0.07 0.62 0.00 0.01 0.00 0.00 0.00 0.00 0.00
## 165 91575 K M 0.01 0.00 0.00 0.02 0.00 0.10 0.15 0.16 0.01 0.67 0.36 0.36 0.36 0.05 0.48
## 166 91576 K M 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 167 91577 K M 0.00 0.00 0.00 0.00 0.00 0.01 0.56 0.04 0.00 0.01 0.00 0.00 0.00 0.00 0.00
## 168 91580 K M 0.00 0.00 0.00 0.00 0.00 0.01 0.41 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.01
## 169 91581 K M 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 170 91582 K F_eggs 0.00 0.00 0.00 0.02 0.00 0.13 0.39 0.11 0.02 0.03 0.00 0.00 0.00 0.00 0.03
## 171 91583 K F_eggs 0.00 0.00 0.00 0.00 0.00 0.02 0.69 0.00 0.00 0.06 0.00 0.00 0.00 0.03 0.02
## 172 91584 K M 0.00 0.10 0.05 0.01 0.00 0.07 0.33 0.00 0.07 0.19 0.06 0.06 0.06 0.01 0.18
## 173 91585 K M 0.00 0.00 0.01 0.00 0.00 0.01 0.22 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.02
## 174 91586 K M 0.00 0.00 0.00 0.00 0.00 0.00 0.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 175 91587 K F 0.00 0.00 0.00 0.00 0.00 0.26 0.34 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.02
## 176 91588 K F_eggs 0.00 0.00 0.01 0.00 0.00 0.02 0.70 0.00 0.00 0.02 0.02 0.02 0.02 0.08 0.01
## 177 91589 K F 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 178 91590 K M 0.00 0.00 0.00 0.00 0.00 0.15 0.98 0.01 0.02 0.05 0.13 0.13 0.13 0.06 0.13
## 179 91591 K M 0.00 0.00 0.00 0.00 0.00 0.17 0.93 0.00 0.02 0.02 0.01 0.01 0.01 0.00 0.02
## 180 91592 K M 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 181 91593 K M 0.00 0.00 0.00 0.00 0.00 0.01 0.52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 182 91594 K F 0.00 0.00 0.00 0.00 0.00 0.04 0.29 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.02
## 183 91595 K M 0.00 0.00 0.00 0.02 0.00 0.24 0.94 0.01 0.03 0.22 0.05 0.05 0.05 0.01 0.31
## 184 91596 K M 0.00 0.00 0.00 0.01 0.00 0.10 0.94 0.08 0.00 0.11 0.02 0.02 0.02 0.09 0.05
## 185 91597 K M 0.00 0.00 0.00 0.00 0.00 0.03 0.94 0.01 0.00 0.10 0.01 0.01 0.01 0.00 0.03
## 186 91598 K F 0.00 0.00 0.04 0.04 0.01 0.41 0.98 0.11 0.04 0.09 0.01 0.01 0.01 0.00 0.08
## 187 91599 K M 0.00 0.00 0.00 0.02 0.00 0.26 0.99 0.18 0.03 0.39 0.04 0.04 0.04 0.03 0.26
## 188 91600 K M 0.00 0.00 0.02 0.00 0.00 0.05 0.76 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00
## 189 91601 K M 0.00 0.00 0.00 0.00 0.00 0.13 0.98 0.01 0.00 0.14 0.01 0.01 0.01 0.00 0.04
## 190 91602 K F 0.00 0.00 0.00 0.00 0.00 0.01 0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 191 91603 K F_eggs 0.00 0.00 0.00 0.01 0.00 0.09 0.58 0.05 0.02 0.10 0.00 0.00 0.00 0.00 0.05
## 192 91604 K M 0.00 0.00 0.00 0.00 0.00 0.06 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 193 91605 K F_eggs 0.00 0.00 0.01 0.00 0.00 0.01 0.38 0.00 0.00 0.10 0.01 0.01 0.01 0.02 0.00
## 194 91606 K M 0.00 0.00 0.00 0.00 0.00 0.13 0.37 0.01 0.00 0.15 0.02 0.02 0.02 0.03 0.03
## 195 91608 K M 0.00 0.00 0.11 0.00 0.00 0.36 0.71 0.07 0.00 0.15 0.00 0.00 0.00 0.01 0.02
## 196 91609 K M 0.00 0.00 0.00 0.00 0.00 0.02 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 197 91610 K F_eggs 0.00 0.00 0.03 0.00 0.00 0.07 0.60 0.03 0.00 0.09 0.02 0.02 0.02 0.08 0.02
## 198 91611 K M 0.00 0.00 0.01 0.02 0.00 0.24 0.74 0.12 0.00 0.37 0.00 0.00 0.00 0.00 0.04
## 3 82401 N M 0.00 0.00 0.00 0.05 0.00 0.07 0.00 0.83 0.00 0.23 0.00 0.00 0.00 0.00 0.02
## 4 82534 N M 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 11 82577 N F 0.00 0.00 0.00 0.27 0.38 0.35 0.02 0.94 0.03 0.14 0.00 0.00 0.00 0.00 0.05
## 12 82578 N M 0.00 0.00 0.00 0.13 0.02 0.12 0.01 0.85 0.01 0.19 0.00 0.00 0.00 0.00 0.05
## 13 91182 N M 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.59 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 14 91186 N F 0.00 0.00 0.00 0.14 0.07 0.28 0.05 0.75 0.01 0.07 0.00 0.00 0.00 0.00 0.02
## 15 91187 N F 0.00 0.00 0.00 0.08 0.01 0.19 0.09 0.78 0.00 0.06 0.00 0.00 0.00 0.00 0.01
## 16 91188 N F 0.00 0.00 0.00 0.08 0.03 0.09 0.00 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.02
## 17 91189 N M 0.00 0.00 0.00 0.09 0.00 0.01 0.00 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 18 91190 N F 0.00 0.00 0.00 0.01 0.00 0.15 0.00 0.90 0.00 0.04 0.00 0.00 0.00 0.00 0.00
## 19 91191 N M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 20 91204 N M 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.49 0.00 0.03 0.00 0.00 0.00 0.00 0.01
## 21 91205 N M 0.00 0.00 0.00 0.46 0.04 0.12 0.00 0.82 0.01 0.21 0.00 0.00 0.00 0.00 0.05
## 22 91206 N F 0.00 0.00 0.31 0.06 0.02 0.59 0.07 0.19 0.02 0.49 0.00 0.00 0.00 0.00 0.07
## 23 91207 N M 0.00 0.00 0.00 0.01 0.00 0.09 0.08 0.86 0.00 0.06 0.00 0.00 0.00 0.00 0.00
## 24 91208 N M 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 25 91209 N F_eggs 0.00 0.00 0.00 0.04 0.00 0.06 0.00 0.48 0.00 0.05 0.00 0.00 0.00 0.00 0.06
## 26 91210 N F 0.00 0.00 0.07 0.11 0.01 0.69 0.42 0.96 0.01 0.27 0.00 0.00 0.00 0.00 0.08
## 27 91211 N M 0.00 0.00 0.00 0.23 0.03 0.00 0.00 0.18 0.02 0.00 0.00 0.00 0.00 0.00 0.01
## 28 91212 N F 0.00 0.00 0.00 0.13 0.05 0.01 0.00 0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 29 91213 N M 0.00 0.00 0.00 0.53 0.03 0.31 0.04 0.81 0.05 0.02 0.00 0.00 0.00 0.00 0.05
## 30 91214 N F_eggs 0.00 0.00 0.00 0.10 0.02 0.01 0.00 0.12 0.04 0.08 0.00 0.00 0.00 0.00 0.09
## 31 91215 N F_eggs 0.00 0.00 0.00 0.76 0.35 0.17 0.00 0.09 0.14 0.00 0.00 0.00 0.00 0.00 0.01
## 32 91221 N F 0.00 0.00 0.02 0.59 0.04 0.60 0.03 0.91 0.02 0.30 0.00 0.00 0.00 0.00 0.09
## 33 91222 N M 0.00 0.00 0.00 0.27 0.00 0.76 0.13 0.95 0.02 0.28 0.00 0.00 0.00 0.00 0.10
## 34 91223 N M 0.00 0.00 0.00 0.09 0.00 0.06 0.00 0.90 0.00 0.06 0.00 0.00 0.00 0.00 0.02
## 35 91224 N M 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.87 0.00 0.04 0.00 0.00 0.00 0.00 0.00
## 36 91225 N F 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.45 0.00 0.01 0.00 0.00 0.00 0.00 0.01
## 37 91226 N M 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 38 91227 N F 0.00 0.00 0.10 0.16 0.01 0.73 0.17 0.76 0.02 0.08 0.00 0.00 0.00 0.00 0.04
## 39 91228 N F 0.00 0.00 0.00 0.02 0.00 0.02 0.00 0.31 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 40 91229 N F 0.00 0.00 0.00 0.12 0.02 0.24 0.10 0.33 0.02 0.06 0.00 0.00 0.00 0.00 0.05
## 41 91230 N M 0.00 0.00 0.00 0.11 0.00 0.24 0.02 0.33 0.00 0.01 0.00 0.00 0.00 0.00 0.01
## 42 91231 N M 0.00 0.00 0.00 0.10 0.00 0.08 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 43 91232 N M 0.00 0.00 0.00 0.05 0.00 0.11 0.03 0.87 0.00 0.01 0.00 0.00 0.00 0.00 0.00
## 44 91233 N M 0.00 0.00 0.00 0.17 0.00 0.33 0.11 0.97 0.00 0.05 0.00 0.00 0.00 0.00 0.01
## 45 91234 N M 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 46 91235 N F 0.00 0.00 0.01 0.03 0.00 0.20 0.00 0.78 0.00 0.04 0.00 0.00 0.00 0.00 0.00
## 47 91236 N F_eggs 0.00 0.00 0.00 0.24 0.01 0.67 0.03 0.68 0.00 0.06 0.00 0.00 0.00 0.00 0.01
## 48 91237 N M 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 49 91239 N M 0.00 0.00 0.02 0.42 0.00 0.69 0.28 0.98 0.00 0.57 0.00 0.00 0.00 0.00 0.15
## 50 91240 N M 0.00 0.00 0.00 0.31 0.00 0.11 0.08 0.81 0.01 0.13 0.00 0.00 0.00 0.00 0.08
## 51 91241 N M 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.70 0.00 0.01 0.00 0.00 0.00 0.00 0.00
## 52 91242 N F 0.00 0.00 0.00 0.01 0.00 0.11 0.01 0.78 0.00 0.04 0.00 0.00 0.00 0.00 0.00
## 53 91243 N F 0.00 0.00 0.00 0.19 0.03 0.49 0.01 0.93 0.00 0.18 0.00 0.00 0.00 0.00 0.01
## 54 91244 N F 0.00 0.00 0.03 0.26 0.01 0.90 0.04 0.80 0.00 0.80 0.00 0.00 0.00 0.00 0.22
## 55 91245 N F 0.00 0.00 0.00 0.02 0.00 0.06 0.00 0.71 0.00 0.02 0.00 0.00 0.00 0.00 0.00
## 56 91246 N F 0.00 0.00 0.03 0.11 0.02 0.65 0.07 0.95 0.01 0.07 0.00 0.00 0.00 0.00 0.02
## 57 91247 N F 0.00 0.00 0.00 0.08 0.01 0.54 0.26 0.99 0.01 0.20 0.00 0.00 0.00 0.00 0.07
## 1 80861 O M 0.00 0.00 0.00 0.18 0.04 0.05 0.01 0.00 0.41 0.00 0.01 0.01 0.01 0.00 0.22
## 199 91617 O F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## 200 91618 O M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00
## 201 91619 O M 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.03
## 202 91620 O F_eggs 0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.61 0.00 0.02 0.02 0.02 0.00 0.10
## 203 91621 O F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 204 91622 O F_eggs 0.00 0.01 0.00 0.00 0.14 0.01 0.00 0.00 0.75 0.00 0.01 0.01 0.01 0.00 0.08
## 205 91623 O F_eggs 0.00 0.00 0.00 0.00 0.24 0.06 0.00 0.00 0.33 0.00 0.00 0.00 0.00 0.00 0.04
## 206 91624 O F_eggs 0.06 0.35 0.00 0.20 0.88 0.11 0.00 0.00 0.98 0.01 0.15 0.15 0.15 0.01 0.58
## 207 91625 O F 0.04 0.01 0.00 0.01 0.17 0.01 0.00 0.00 0.44 0.01 0.15 0.15 0.15 0.00 0.42
## 208 91626 O F 0.00 0.05 0.00 0.48 0.16 0.17 0.04 0.02 0.64 0.04 0.26 0.26 0.26 0.06 0.48
## 209 91627 O F 0.00 0.01 0.00 0.73 0.91 0.17 0.02 0.02 0.77 0.04 0.02 0.02 0.02 0.00 0.42
## 210 91628 O M 0.00 0.01 0.00 0.82 0.60 0.03 0.00 0.01 0.76 0.02 0.01 0.01 0.01 0.00 0.45
## 211 91629 O F 0.00 0.00 0.00 0.48 0.21 0.21 0.15 0.03 0.88 0.09 0.26 0.26 0.26 0.01 0.89
## 212 91630 O F_eggs 0.00 0.01 0.00 0.60 0.55 0.04 0.00 0.00 0.82 0.00 0.00 0.00 0.00 0.00 0.21
## 213 91631 O F_eggs 0.00 0.00 0.00 0.02 0.01 0.00 0.00 0.00 0.43 0.00 0.00 0.00 0.00 0.00 0.05
## 214 91632 O F_eggs 0.04 0.07 0.01 0.06 0.40 0.07 0.00 0.00 0.47 0.01 0.01 0.01 0.01 0.00 0.19
## 215 91633 O F_eggs 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.04
## 216 91634 O F_eggs 0.01 0.02 0.00 0.01 0.03 0.00 0.00 0.00 0.59 0.00 0.11 0.11 0.11 0.00 0.37
## 217 91635 O F 0.00 0.00 0.00 0.04 0.73 0.02 0.00 0.01 0.72 0.00 0.00 0.00 0.00 0.00 0.12
## 218 91636 O M 0.00 0.01 0.00 0.01 0.07 0.00 0.00 0.00 0.75 0.00 0.02 0.02 0.02 0.00 0.17
## 219 91637 O M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.03
## 220 91638 O F_eggs 0.00 0.00 0.00 0.02 0.05 0.01 0.02 0.00 0.32 0.01 0.01 0.01 0.01 0.00 0.09
## 221 91643 O F_eggs 0.00 0.00 0.00 0.13 0.13 0.00 0.00 0.00 0.22 0.01 0.00 0.00 0.00 0.00 0.09
## 222 91644 O M 0.00 0.00 0.00 0.12 0.01 0.00 0.00 0.00 0.62 0.00 0.01 0.01 0.01 0.00 0.21
## 223 91645 O M 0.04 0.19 0.00 0.01 0.03 0.01 0.00 0.00 0.12 0.13 0.19 0.19 0.19 0.00 0.32
## 224 91651 P M 0.00 0.00 0.00 0.02 0.00 0.07 0.32 0.13 0.02 0.62 0.00 0.00 0.00 0.00 0.25
## 225 91652 P F_eggs 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.53 0.00 0.00 0.00 0.00 0.03
## 226 91653 P F_eggs 0.00 0.00 0.00 0.00 0.00 0.03 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00
## 227 91654 P M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.53 0.02 0.02 0.02 0.01 0.03
## 228 91655 P M 0.00 0.00 0.00 0.00 0.00 0.01 0.06 0.03 0.00 0.42 0.02 0.02 0.02 0.00 0.03
## 229 91656 P M 0.00 0.00 0.00 0.00 0.00 0.09 0.40 0.06 0.00 0.35 0.00 0.00 0.00 0.00 0.05
## 230 91657 P M 0.00 0.00 0.34 0.08 0.00 0.78 0.18 0.44 0.01 0.98 0.01 0.01 0.01 0.00 0.26
## 231 91658 P M 0.01 0.00 0.01 0.01 0.00 0.20 0.17 0.22 0.02 0.97 0.12 0.12 0.12 0.01 0.64
## 232 91659 P F_eggs 0.00 0.00 0.05 0.01 0.00 0.13 0.01 0.05 0.00 0.24 0.00 0.00 0.00 0.00 0.06
## 233 91660 P M 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.00 0.00 0.48 0.01 0.01 0.01 0.00 0.05
## 234 91661 P M 0.00 0.00 0.01 0.04 0.00 0.09 0.13 0.20 0.00 0.44 0.00 0.00 0.00 0.00 0.02
## 235 91662 P M 0.00 0.00 0.02 0.02 0.00 0.11 0.01 0.10 0.00 0.65 0.01 0.01 0.01 0.00 0.09
## 236 91673 P F 0.01 0.01 0.05 0.07 0.00 0.28 0.06 0.04 0.02 0.99 0.12 0.12 0.12 0.09 0.67
## 237 91674 P F_eggs 0.09 0.19 0.05 0.16 0.06 0.42 0.06 0.03 0.37 0.84 0.71 0.71 0.71 0.44 0.95
## 238 91675 P F_eggs 0.02 0.00 0.01 0.16 0.04 0.45 0.10 0.15 0.17 0.97 0.12 0.12 0.12 0.01 0.87
## 239 91676 P F 0.00 0.01 0.01 0.00 0.00 0.02 0.00 0.00 0.00 0.57 0.02 0.02 0.02 0.00 0.16
## 240 91677 P F 0.02 0.00 0.00 0.06 0.02 0.14 0.10 0.02 0.23 0.50 0.26 0.26 0.26 0.07 0.85
## 243 91680 Q3 M 0.01 0.07 0.00 0.28 0.01 0.02 0.00 0.00 0.04 0.03 0.29 0.29 0.29 0.01 0.29
## 248 91685 Q3 F 0.01 0.01 0.00 0.00 0.00 0.01 0.06 0.00 0.10 0.04 0.53 0.53 0.53 0.04 0.24
## 249 91688 Q3 M 0.28 0.44 0.01 0.00 0.00 0.02 0.03 0.00 0.01 0.06 0.35 0.35 0.35 0.03 0.13
## 250 91690 Q3 F_eggs 0.04 0.02 0.00 0.00 0.00 0.07 0.06 0.00 0.04 0.32 0.40 0.40 0.40 0.03 0.42
## 251 91691 Q3 F_eggs 0.35 0.12 0.00 0.01 0.01 0.07 0.06 0.00 0.12 0.49 0.92 0.92 0.92 0.63 0.87
## 252 91692 Q3 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.09 0.09 0.09 0.08 0.04
## 253 91693 Q3 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.00 0.01
## 254 91694 Q3 M 0.00 0.00 0.00 0.00 0.00 0.01 0.29 0.00 0.00 0.03 0.11 0.11 0.11 0.35 0.08
## 263 91704 Q3 M 0.01 0.00 0.00 0.06 0.00 0.03 0.03 0.01 0.02 0.10 0.14 0.14 0.14 0.00 0.35
## 268 91709 Q3 M 0.33 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.02 0.85 0.85 0.85 0.42 0.38
## 269 91710 Q3 M 0.17 0.22 0.00 0.01 0.02 0.01 0.02 0.00 0.22 0.03 0.94 0.94 0.94 0.49 0.61
## 270 91711 Q3 M 0.53 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.78 0.78 0.78 0.04 0.07
## 271 91712 Q3 M 0.07 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.23 0.23 0.23 0.02 0.00
## 272 91713 Q3 F 0.36 0.03 0.00 0.00 0.00 0.02 0.00 0.00 0.01 0.03 0.58 0.58 0.58 0.08 0.32
## 273 91714 Q3 F 0.64 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.55 0.55 0.55 0.00 0.07
## 274 91715 Q3 F_eggs 0.23 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.05 0.00 0.51 0.51 0.51 0.02 0.19
## 275 91716 Q3 M 0.07 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.21 0.21 0.01 0.01
## 276 91717 Q3 M 0.10 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.13 0.13 0.00 0.00
## 290 91731 Q3 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 291 91732 Q3 F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 292 91734 Q3 F 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.24 0.24 0.24 0.07 0.01
## 293 91735 Q3 F 0.30 0.18 0.01 0.00 0.00 0.02 0.03 0.00 0.02 0.34 0.78 0.78 0.78 0.09 0.44
## 294 91736 Q3 F 0.02 0.03 0.00 0.00 0.00 0.02 0.03 0.00 0.02 0.20 0.81 0.81 0.81 0.08 0.32
## 265 91706 Q4 F 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.30 0.30 0.36 0.00
## 266 91707 Q4 F_eggs 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.09 0.09 0.00 0.00
## 404 Cae731 Q4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.03 0.00
## 405 Cae732 Q4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00
## 406 Cae733 Q4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 407 Cae734 Q4 F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 408 Cae735 Q4 M 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.00
## 409 Cae736 Q4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 410 Cae737 Q4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.03 0.00
## 416 Cae760 Q4 M 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00
## 417 Cae761 Q4 M 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.05 0.17 0.00
## 418 Cae762 Q4 M 0.01 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.03 0.00
## 419 Cae763 Q4 M 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.06 0.00
## 420 Cae764 Q4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 241 91678 Q5 F_eggs 0.07 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.28 0.28 0.28 0.69 0.02
## 242 91679 Q5 F 0.02 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.03 0.04 0.00
## 244 91681 Q5 F_eggs 0.16 0.04 0.03 0.00 0.00 0.09 0.12 0.00 0.01 0.57 0.46 0.46 0.46 0.61 0.44
## 245 91682 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 246 91683 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 247 91684 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.12 0.12 0.32 0.00
## 255 91695 Q5 F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 256 91696 Q5 F_eggs 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.13 0.13 0.11 0.01
## 257 91697 Q5 F_eggs 0.03 0.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.13 0.13 0.06 0.00
## 267 91708 Q5 F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00
## 277 91718 Q5 F 0.19 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.48 0.48 0.48 0.41 0.01
## 278 91719 Q5 F 0.00 0.09 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.22 0.22 0.22 0.87 0.02
## 279 91720 Q5 M 0.10 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.23 0.23 0.23 0.36 0.16
## 280 91721 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.25 0.00
## 281 91722 Q5 M 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.34 0.00
## 282 91723 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 283 91724 Q5 F_eggs 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.05 0.25 0.00
## 284 91725 Q5 F_eggs 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.10 0.00
## 285 91726 Q5 F_eggs 0.00 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.13 0.00
## 286 91727 Q5 M 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.05 0.00 0.00
## 287 91728 Q5 M 0.00 0.18 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.25 0.00
## 288 91729 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00
## 289 91730 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00
## 295 91737 Q5 F_eggs 0.01 0.06 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.23 0.23 0.23 0.84 0.01
## 296 91738 Q5 M 0.00 0.05 0.13 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.11 0.11 0.11 0.48 0.00
## 297 91739 Q5 M 0.00 0.01 0.12 0.00 0.00 0.01 0.06 0.00 0.00 0.04 0.11 0.11 0.11 0.64 0.02
## 298 91740 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.03 0.45 0.00
## 299 91742 Q5 F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 300 91743 Q5 F 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.02 0.00
## 301 91744 Q5 M 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.16 0.00
## 302 91745 Q5 F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 303 91747 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 304 91748 Q5 F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 305 91749 Q5 F_eggs 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.05 0.25 0.00
## 306 91750 Q5 F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.05 0.06 0.00
## 307 91751 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 308 91752 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00
## 309 91753 Q5 M 0.00 0.04 0.11 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.02 0.02 0.02 0.14 0.00
## 310 91754 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.05 0.00
## 311 91755 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 312 91756 Q5 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.31 0.00
## 313 91757 Q5 M 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.05 0.00
## 7 82537 R F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.09 0.09 0.48 0.00
## 8 82538 R M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.25 0.00
## 314 91767 R F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.07 0.07 0.33 0.01
## 315 91768 R M 0.00 0.02 0.02 0.00 0.00 0.06 0.46 0.00 0.00 0.35 0.19 0.19 0.19 0.63 0.12
## 5 82535 S F 0.00 0.01 0.00 0.00 0.00 0.02 0.00 0.00 0.01 0.61 0.05 0.05 0.05 0.00 0.35
## 6 82536 S M 0.08 0.02 0.00 0.26 0.02 0.32 0.07 0.03 0.09 0.24 0.48 0.48 0.48 0.01 0.64
## 316 91770 S M 0.16 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.13 0.28 0.28 0.28 0.00 0.37
## 317 91771 S F 0.01 0.00 0.00 0.01 0.00 0.01 0.00 0.02 0.03 0.27 0.07 0.07 0.07 0.00 0.44
## 318 91772 S M 0.31 0.01 0.00 0.01 0.00 0.05 0.06 0.00 0.04 0.45 0.76 0.76 0.76 0.03 0.71
## 319 91775 S M 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.16 0.16 0.16 0.04 0.18
## 320 91776 S M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.14 0.14 0.02 0.01
## 321 91777 S M 0.17 0.02 0.00 0.02 0.00 0.11 0.21 0.00 0.04 0.28 0.78 0.78 0.78 0.21 0.77
## 322 91778 S M 0.04 0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.06 0.77 0.77 0.77 0.26 0.42
## 323 91779 S M 0.18 0.16 0.01 0.25 0.01 0.26 0.11 0.03 0.19 0.91 0.84 0.84 0.84 0.21 0.98
## 324 91781 S F 0.03 0.00 0.00 0.01 0.00 0.16 0.04 0.03 0.00 0.43 0.11 0.11 0.11 0.00 0.29
## 325 91782 S M 0.00 0.00 0.00 0.13 0.00 0.21 0.10 0.11 0.01 0.67 0.05 0.05 0.05 0.00 0.42
## 326 91783 S M 0.00 0.00 0.00 0.35 0.00 0.31 0.12 0.11 0.11 0.58 0.14 0.14 0.14 0.00 0.65
## 327 91784 S M 0.01 0.00 0.00 0.00 0.00 0.10 0.03 0.00 0.00 0.09 0.24 0.24 0.24 0.06 0.22
## 328 91785 S M 0.00 0.00 0.00 0.25 0.00 0.37 0.06 0.16 0.06 0.36 0.04 0.04 0.04 0.00 0.64
## 329 91786 S M 0.00 0.00 0.00 0.40 0.00 0.26 0.04 0.11 0.14 0.31 0.07 0.07 0.07 0.00 0.57
## 330 91787 S M 0.00 0.00 0.00 0.15 0.01 0.30 0.04 0.05 0.08 0.28 0.03 0.03 0.03 0.00 0.46
## 331 91790 S F_eggs 0.00 0.00 0.00 0.11 0.26 0.13 0.00 0.04 0.14 0.01 0.00 0.00 0.00 0.00 0.15
## 332 91791 S M 0.01 0.02 0.02 0.11 0.01 0.70 0.47 0.05 0.18 0.61 0.11 0.11 0.11 0.10 0.81
## 333 91793 S M 0.01 0.02 0.00 0.21 0.02 0.32 0.30 0.03 0.47 0.34 0.68 0.68 0.68 0.16 0.87
## 334 91794 S F_eggs 0.14 0.07 0.00 0.53 0.21 0.45 0.16 0.15 0.47 0.77 0.62 0.62 0.62 0.25 0.99
## 335 91795 S F 0.21 0.01 0.00 0.04 0.10 0.02 0.00 0.01 0.15 0.32 0.16 0.16 0.16 0.02 0.67
## 336 91796 S M 0.00 0.04 0.00 0.54 0.09 0.04 0.00 0.03 0.11 0.13 0.03 0.03 0.03 0.00 0.22
## 337 91797 S M 0.32 0.06 0.00 0.11 0.02 0.09 0.00 0.00 0.03 0.33 0.10 0.10 0.10 0.00 0.54
## 338 91798 S F 0.03 0.11 0.00 0.11 0.03 0.01 0.00 0.00 0.40 0.02 0.16 0.16 0.16 0.01 0.53
## 339 91799 S F_eggs 0.00 0.01 0.00 0.00 0.01 0.01 0.00 0.00 0.11 0.17 0.01 0.01 0.01 0.01 0.26
## 340 91800 S F_eggs 0.00 0.00 0.00 0.02 0.02 0.01 0.00 0.00 0.18 0.04 0.03 0.03 0.03 0.00 0.61
## 341 91801 S M 0.00 0.02 0.00 0.00 0.00 0.01 0.01 0.00 0.48 0.00 0.02 0.02 0.02 0.00 0.19
## 342 91802 S F_eggs 0.00 0.00 0.00 0.00 0.01 0.02 0.00 0.00 0.58 0.00 0.00 0.00 0.00 0.00 0.14
## 343 91803 S F_eggs 0.00 0.01 0.00 0.00 0.09 0.00 0.00 0.00 0.54 0.00 0.01 0.01 0.01 0.00 0.11
## 344 91804 S F 0.08 0.13 0.01 0.19 0.01 0.14 0.00 0.00 0.08 0.09 0.52 0.52 0.52 0.08 0.52
## 345 91805 S F 0.05 0.06 0.01 0.06 0.07 0.10 0.00 0.01 0.23 0.10 0.19 0.19 0.19 0.00 0.38
## 346 91806 S F 0.01 0.02 0.00 0.13 0.01 0.02 0.01 0.00 0.32 0.50 0.11 0.11 0.11 0.00 0.79
## 347 91807 S M 0.01 0.02 0.06 0.43 0.20 0.84 0.13 0.12 0.31 0.79 0.04 0.04 0.04 0.00 0.74
## 348 91808 S F_eggs 0.15 0.12 0.00 0.69 0.14 0.69 0.06 0.06 0.34 0.42 0.75 0.75 0.75 0.04 0.94
## 349 91809 S F 0.03 0.01 0.00 0.20 0.01 0.42 0.19 0.07 0.20 0.47 0.74 0.74 0.74 0.56 0.96
## 350 91810 S F 0.17 0.15 0.00 0.02 0.07 0.14 0.17 0.00 0.75 0.11 0.72 0.72 0.72 0.22 0.93
## 351 91811 S M 0.02 0.12 0.00 0.09 0.32 0.06 0.03 0.00 0.96 0.05 0.17 0.17 0.17 0.03 0.77
## 352 91812 S M 0.07 0.08 0.00 0.03 0.07 0.03 0.00 0.00 0.47 0.04 0.23 0.23 0.23 0.01 0.69
## 353 91813 S F_eggs 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.27 0.27 0.27 0.04 0.15
## 354 91814 S M 0.01 0.03 0.00 0.23 0.26 0.17 0.01 0.02 0.48 0.12 0.11 0.11 0.11 0.10 0.76
## 355 91815 S M 0.05 0.03 0.00 0.00 0.02 0.00 0.00 0.00 0.22 0.01 0.38 0.38 0.38 0.01 0.34
## 356 91816 S M 0.01 0.00 0.00 0.01 0.06 0.01 0.01 0.00 0.55 0.02 0.08 0.08 0.08 0.00 0.45
## 357 91817 S M 0.02 0.01 0.00 0.07 0.00 0.67 0.23 0.03 0.26 0.71 0.39 0.39 0.39 0.02 0.92
## 358 91818 S F 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.17 0.08 0.08 0.08 0.00 0.20
## 359 91819 S M 0.00 0.00 0.00 0.35 0.38 0.29 0.13 0.04 0.95 0.03 0.00 0.00 0.00 0.00 0.35
## 360 91820 S F_eggs 0.00 0.00 0.00 0.00 0.02 0.03 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.02
## 361 91821 S F 0.00 0.00 0.00 0.03 0.90 0.03 0.01 0.00 0.64 0.01 0.00 0.00 0.00 0.00 0.10
## 362 91822 S M 0.00 0.00 0.00 0.01 0.22 0.00 0.00 0.00 0.62 0.00 0.00 0.00 0.00 0.00 0.07
## 363 91823 S M 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.56 0.00 0.01 0.01 0.01 0.00 0.22
## 364 91826 S F 0.00 0.00 0.00 0.01 0.00 0.12 0.06 0.21 0.00 0.93 0.00 0.00 0.00 0.00 0.14
## 365 91827 S F 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.02
## 366 91828 S F 0.00 0.00 0.00 0.09 0.00 0.33 0.02 0.30 0.01 0.35 0.03 0.03 0.03 0.00 0.38
## 367 91829 S F_eggs 0.00 0.01 0.00 0.41 0.16 0.41 0.01 0.02 0.66 0.15 0.02 0.02 0.02 0.00 0.83
## 368 91830 S F_eggs 0.00 0.01 0.00 0.45 0.37 0.14 0.00 0.06 0.80 0.08 0.02 0.02 0.02 0.01 0.73
## 369 91831 S F 0.01 0.05 0.00 0.06 0.22 0.02 0.00 0.00 0.38 0.07 0.02 0.02 0.02 0.01 0.40
## 370 91832 S M 0.00 0.00 0.00 0.15 0.01 0.03 0.00 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.16
## 371 91833 S M 0.08 0.16 0.00 0.27 0.10 0.04 0.04 0.00 0.76 0.11 0.74 0.74 0.74 0.05 0.91
## 372 91838 S F_eggs 0.00 0.00 0.01 0.32 0.02 0.42 0.31 0.16 0.29 0.71 0.28 0.28 0.28 0.07 0.85
## 373 91839 S F 0.05 0.01 0.00 0.03 0.05 0.11 0.01 0.01 0.19 0.20 0.08 0.08 0.08 0.02 0.51
## 374 91842 S M 0.36 0.21 0.00 0.01 0.01 0.03 0.06 0.00 0.23 0.30 0.98 0.98 0.98 0.48 0.87
## 375 91843 S F 0.01 0.00 0.00 0.08 0.20 0.12 0.35 0.06 0.64 0.72 0.28 0.28 0.28 0.04 0.91
## 376 91844 S F 0.01 0.11 0.01 0.00 0.00 0.16 0.03 0.00 0.01 0.08 0.16 0.16 0.16 0.51 0.22
## 377 91845 S F 0.68 0.25 0.00 0.03 0.01 0.04 0.00 0.00 0.17 0.20 0.73 0.73 0.73 0.10 0.88
## 378 91846 S M 0.02 0.03 0.00 0.20 0.01 0.10 0.02 0.00 0.22 0.05 0.44 0.44 0.44 0.02 0.68
## 379 91847 S F 0.66 0.32 0.00 0.02 0.08 0.07 0.00 0.00 0.13 0.03 0.80 0.80 0.80 0.25 0.58
## 380 91848 S M 0.22 0.19 0.00 0.47 0.24 0.31 0.12 0.02 0.71 0.40 0.92 0.92 0.92 0.18 0.98
## 258 91698 weeksi M 0.01 0.00 0.00 0.04 0.01 0.14 0.00 0.07 0.05 0.29 0.02 0.02 0.02 0.00 0.39
## 259 91699 weeksi M 0.00 0.00 0.00 0.05 0.00 0.28 0.11 0.44 0.01 0.62 0.01 0.01 0.01 0.00 0.33
## 262 91703 weeksi M 0.01 0.10 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.00 0.02
## 264 91705 weeksi M 0.00 0.00 0.02 0.01 0.00 0.09 0.08 0.00 0.03 0.18 0.02 0.02 0.02 0.00 0.10
## 440 Cae794 weeksi M 0.29 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.60 0.60 0.02 0.10
## 441 Cae795 weeksi M 0.40 0.09 0.00 0.07 0.03 0.19 0.09 0.00 0.43 0.55 0.70 0.70 0.70 0.08 0.96
## 442 Cae796 weeksi F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.17 0.03 0.09 0.09 0.09 0.00 0.28
## 466 Cae828 weeksi F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.00 0.01
## 467 Cae829 weeksi M 0.00 0.00 0.00 0.02 0.00 0.02 0.06 0.00 0.01 0.38 0.12 0.12 0.12 0.00 0.20
## 468 Cae830 weeksi F 0.04 0.00 0.00 0.00 0.00 0.01 0.02 0.00 0.00 0.71 0.19 0.19 0.19 0.01 0.38
## 469 Cae834 weeksi F_eggs 0.00 0.00 0.20 0.00 0.00 0.10 0.08 0.00 0.00 0.27 0.05 0.05 0.05 0.07 0.04
## 470 Cae835 weeksi F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.01 0.01 0.01 0.00 0.01
## 471 Cae836 weeksi F_eggs 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.01 0.00 0.74 0.00 0.00 0.00 0.00 0.17
## 472 Cae837 weeksi M 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.08 0.06 0.06 0.06 0.01 0.07
## 473 Cae838 weeksi F_eggs 0.00 0.05 0.04 0.00 0.00 0.18 0.47 0.00 0.01 0.61 0.45 0.45 0.45 0.35 0.32
## 474 Cae839 weeksi M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00
## 475 Cae840 weeksi F 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.25 0.03 0.03 0.03 0.00 0.08
## 476 Cae842 weeksi M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00
## 477 Cae846 weeksi M 0.00 0.00 0.00 0.07 0.00 0.05 0.09 0.01 0.01 0.31 0.08 0.08 0.08 0.00 0.47
## 478 Cae847 weeksi F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 479 Cae848 weeksi F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01
## 480 Cae849 weeksi M 0.00 0.00 0.00 0.01 0.00 0.11 0.11 0.00 0.01 0.53 0.21 0.21 0.21 0.01 0.42
## 481 Cae850 weeksi F_eggs 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.41 0.05 0.05 0.05 0.05 0.04
## 482 Cae851 weeksi M 0.00 0.00 0.00 0.00 0.00 0.01 0.07 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.09
## 483 Cae852 weeksi M 0.00 0.01 0.00 0.08 0.00 0.13 0.13 0.00 0.04 0.09 0.20 0.20 0.20 0.03 0.38
## 484 Cae853 weeksi M 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00
## 485 Cae854 weeksi M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 447 Cae804 X2 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 448 Cae805 X2 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 449 Cae806 X2 M 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02 0.04 0.00
## 426 Cae776 X3 M 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.01 0.00
## 427 Cae777 X3 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 428 Cae778 X3 F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.00
## 429 Cae779 X3 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 430 Cae780 X3 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.00
## 431 Cae781 X3 F 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 432 Cae782 X3 F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 433 Cae783 X3 M 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.01 0.00
## 434 Cae784 X3 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.03 0.00
## 435 Cae785 X3 M 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04 0.04 0.00 0.00
## 411 Cae738 X4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 412 Cae739 X4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 413 Cae740 X4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 414 Cae741 X4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 415 Cae742 X4 M 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 443 Cae797 X5 F 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.01
## 444 Cae798 X5 M 0.01 0.01 0.04 0.11 0.03 0.09 0.05 0.17 0.19 0.71 0.11 0.11 0.11 0.00 0.58
## 445 Cae799 X5 F_eggs 0.00 0.00 0.00 0.01 0.00 0.21 0.12 0.07 0.02 0.06 0.02 0.02 0.02 0.01 0.17
## 446 Cae800 X5 M 0.00 0.00 0.01 0.07 0.00 0.56 0.60 0.09 0.01 0.02 0.03 0.03 0.03 0.01 0.05
## 436 Cae786 X8 M 0.25 0.16 0.00 0.71 0.42 0.44 0.03 0.05 0.69 0.37 0.36 0.36 0.36 0.01 0.95
## 437 Cae787 X8 M 0.01 0.21 0.00 0.06 0.01 0.02 0.02 0.00 0.69 0.01 0.47 0.47 0.47 0.12 0.70
## 438 Cae788 X8 M 0.01 0.15 0.00 0.18 0.03 0.04 0.05 0.01 0.46 0.06 0.82 0.82 0.82 0.30 0.65
## 439 Cae789 X8 M 0.17 0.15 0.00 0.51 0.37 0.21 0.08 0.02 0.62 0.13 0.66 0.66 0.66 0.07 0.86
## 451 Cae808 X8 F_eggs 0.01 0.15 0.00 0.33 0.04 0.07 0.13 0.00 0.70 0.18 0.75 0.75 0.75 0.29 0.88
## 452 Cae809 X8 M 0.01 0.47 0.00 0.06 0.00 0.02 0.05 0.00 0.14 0.01 0.56 0.56 0.56 0.12 0.24
## 456 Cae813 X8 M 0.00 0.06 0.00 0.00 0.00 0.00 0.01 0.00 0.26 0.01 0.27 0.27 0.27 0.07 0.36
## 457 Cae814 X8 M 0.00 0.03 0.00 0.00 0.00 0.00 0.01 0.00 0.08 0.00 0.69 0.69 0.69 0.32 0.23
## 458 Cae815 X8 F_eggs 0.00 0.10 0.00 0.11 0.07 0.02 0.00 0.00 0.33 0.00 0.03 0.03 0.03 0.02 0.17
## 459 Cae816 X8 F_eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 460 Cae817 X8 M 0.00 0.13 0.00 0.01 0.00 0.00 0.00 0.00 0.04 0.00 0.21 0.21 0.21 0.05 0.06
## 461 Cae823 X8 M 0.03 0.21 0.00 0.04 0.12 0.03 0.06 0.00 0.42 0.19 0.39 0.39 0.39 0.12 0.61
## 462 Cae824 X8 F_eggs 0.00 0.01 0.00 0.01 0.01 0.02 0.00 0.00 0.05 0.00 0.01 0.01 0.01 0.15 0.08
## 463 Cae825 X8 M 0.00 0.00 0.00 0.01 0.00 0.03 0.01 0.00 0.01 0.00 0.03 0.03 0.03 0.22 0.04
## 464 Cae826 X8 M 0.00 0.01 0.00 0.01 0.02 0.00 0.00 0.00 0.15 0.02 0.31 0.31 0.31 0.19 0.31
## 465 Cae827 X8 M 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.05 0.05 0.05 0.10 0.03
## 421 Cae765 X9 F_eggs 0.00 0.00 0.00 0.11 0.00 0.52 0.61 0.52 0.01 0.22 0.03 0.03 0.03 0.01 0.21
## 422 Cae766 X9 M 0.03 0.00 0.00 0.33 0.01 0.10 0.00 0.03 0.10 0.04 0.28 0.28 0.28 0.01 0.67
## 423 Cae767 X9 F_eggs 0.00 0.00 0.00 0.86 0.36 0.44 0.02 0.89 0.26 0.23 0.00 0.00 0.00 0.00 0.36
## 424 Cae768 X9 M 0.00 0.00 0.01 0.37 0.00 0.40 0.18 0.53 0.01 0.06 0.00 0.00 0.00 0.01 0.05
## 425 Cae769 X9 M 0.01 0.08 0.00 0.64 0.19 0.70 0.41 0.21 0.75 0.44 0.52 0.52 0.52 0.09 0.92
## 453 Cae810 X9 F 0.00 0.00 0.03 0.73 0.10 0.96 0.23 0.78 0.19 0.62 0.03 0.03 0.03 0.00 0.61
## 454 Cae811 X9 F 0.00 0.00 0.01 0.20 0.05 0.20 0.00 0.27 0.03 0.19 0.00 0.00 0.00 0.00 0.10
## 455 Cae812 X9 M 0.00 0.00 0.00 0.41 0.01 0.48 0.08 0.37 0.01 0.04 0.01 0.01 0.01 0.00 0.10
## typweeksi typX2 typX3 typX4 typX5 typX8 typX9
## 69 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 70 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 71 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 72 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 73 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 74 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 75 0.00 0.00 0.00 0.00 0.00 0.02 0.00
## 76 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 77 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## 78 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 79 0.01 0.00 0.02 0.00 0.00 0.03 0.00
## 80 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 81 0.01 0.00 0.04 0.00 0.00 0.12 0.00
## 82 0.00 0.00 0.00 0.00 0.00 0.03 0.01
## 83 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 84 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## 85 0.00 0.00 0.03 0.00 0.00 0.43 0.02
## 86 0.02 0.00 0.05 0.00 0.00 0.01 0.00
## 87 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 88 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 89 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 90 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 91 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 92 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 93 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 94 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 95 0.01 0.00 0.00 0.00 0.00 0.01 0.00
## 96 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 97 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 98 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 99 0.00 0.00 0.00 0.00 0.00 0.05 0.00
## 100 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 101 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 102 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 103 0.00 0.00 0.00 0.00 0.00 0.06 0.00
## 104 0.02 0.00 0.00 0.00 0.01 0.00 0.00
## 105 0.00 0.00 0.00 0.00 0.00 0.05 0.00
## 106 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 107 0.00 0.01 0.02 0.00 0.00 0.02 0.00
## 108 0.00 0.00 0.00 0.00 0.03 0.01 0.00
## 109 0.00 0.00 0.01 0.00 0.00 0.04 0.00
## 110 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 111 0.00 0.00 0.00 0.00 0.00 0.04 0.00
## 112 0.00 0.00 0.00 0.00 0.01 0.35 0.01
## 113 0.00 0.00 0.00 0.00 0.00 0.26 0.00
## 114 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 115 0.00 0.04 0.01 0.00 0.00 0.10 0.00
## 116 0.00 0.00 0.01 0.00 0.00 0.00 0.00
## 117 0.00 0.00 0.01 0.00 0.00 0.02 0.00
## 118 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 119 0.00 0.00 0.03 0.01 0.00 0.01 0.00
## 120 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 121 0.00 0.00 0.00 0.00 0.00 0.37 0.00
## 122 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 123 0.00 0.00 0.01 0.00 0.00 0.10 0.00
## 124 0.23 0.00 0.01 0.00 0.00 0.02 0.00
## 125 0.03 0.00 0.00 0.00 0.01 0.15 0.02
## 126 0.00 0.00 0.00 0.00 0.00 0.02 0.00
## 127 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 58 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 59 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 60 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 61 0.01 0.00 0.00 0.00 0.01 0.00 0.00
## 62 0.00 0.00 0.00 0.00 0.00 0.00 0.02
## 63 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 64 0.01 0.00 0.00 0.00 0.00 0.00 0.01
## 65 0.01 0.00 0.00 0.00 0.03 0.02 0.07
## 66 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 67 0.07 0.00 0.00 0.00 0.01 0.00 0.00
## 68 0.02 0.00 0.00 0.00 0.14 0.00 0.00
## 128 0.00 0.00 0.00 0.00 0.00 0.00 0.02
## 129 0.00 0.00 0.00 0.00 0.00 0.00 0.02
## 135 0.12 0.00 0.00 0.00 0.18 0.44 0.46
## 383 0.05 0.00 0.00 0.00 0.54 0.45 0.93
## 384 0.00 0.00 0.00 0.00 0.00 0.01 0.03
## 385 0.27 0.00 0.00 0.00 0.77 0.66 0.54
## 386 0.00 0.00 0.00 0.00 0.02 0.04 0.15
## 387 0.08 0.00 0.00 0.00 0.12 0.31 0.41
## 388 0.01 0.00 0.00 0.00 0.02 0.18 0.35
## 389 0.00 0.00 0.00 0.00 0.02 0.00 0.35
## 390 0.04 0.00 0.00 0.00 0.39 0.34 0.06
## 391 0.00 0.00 0.00 0.00 0.02 0.03 0.41
## 392 0.00 0.00 0.00 0.00 0.03 0.00 0.46
## 393 0.01 0.00 0.00 0.00 0.38 0.04 0.90
## 394 0.03 0.00 0.00 0.00 0.07 0.04 0.62
## 395 0.00 0.00 0.00 0.00 0.07 0.00 0.27
## 396 0.01 0.00 0.00 0.00 0.22 0.04 0.61
## 397 0.00 0.00 0.00 0.00 0.01 0.00 0.11
## 398 0.00 0.00 0.00 0.00 0.01 0.00 0.14
## 130 0.00 0.00 0.00 0.00 0.13 0.04 0.05
## 131 0.00 0.00 0.00 0.00 0.01 0.06 0.03
## 132 0.00 0.00 0.00 0.00 0.02 0.07 0.20
## 133 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 134 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 136 0.00 0.00 0.00 0.00 0.03 0.00 0.08
## 137 0.19 0.00 0.00 0.00 0.74 0.04 0.73
## 138 0.00 0.00 0.00 0.00 0.09 0.00 0.31
## 139 0.41 0.00 0.00 0.00 0.12 0.10 0.61
## 9 0.01 0.00 0.00 0.00 0.01 0.00 0.01
## 10 0.83 0.00 0.00 0.00 0.14 0.03 0.01
## 140 0.33 0.00 0.00 0.00 0.55 0.00 0.05
## 141 0.46 0.00 0.00 0.00 0.61 0.01 0.59
## 142 0.01 0.00 0.00 0.00 0.03 0.01 0.47
## 143 0.00 0.00 0.00 0.00 0.04 0.00 0.01
## 144 0.35 0.00 0.00 0.00 0.85 0.15 0.73
## 145 0.38 0.00 0.00 0.00 0.15 0.05 0.02
## 146 0.43 0.00 0.00 0.00 0.93 0.21 0.74
## 147 0.07 0.00 0.00 0.00 0.01 0.00 0.00
## 148 0.00 0.00 0.00 0.00 0.01 0.00 0.02
## 149 0.03 0.00 0.00 0.00 0.26 0.02 0.03
## 150 0.68 0.00 0.00 0.00 0.79 0.63 0.57
## 151 0.02 0.00 0.00 0.00 0.03 0.00 0.00
## 152 0.01 0.00 0.00 0.00 0.06 0.00 0.00
## 153 0.00 0.00 0.00 0.00 0.13 0.01 0.02
## 154 0.01 0.00 0.00 0.00 0.02 0.00 0.00
## 155 0.00 0.00 0.00 0.00 0.02 0.00 0.00
## 156 0.01 0.00 0.00 0.00 0.01 0.00 0.01
## 157 0.01 0.00 0.00 0.00 0.01 0.00 0.00
## 158 0.00 0.00 0.00 0.00 0.02 0.00 0.00
## 159 0.27 0.00 0.00 0.00 0.75 0.01 0.27
## 160 0.26 0.00 0.00 0.00 0.04 0.00 0.00
## 161 0.00 0.00 0.00 0.00 0.34 0.00 0.02
## 162 0.00 0.00 0.00 0.00 0.06 0.00 0.00
## 163 0.00 0.00 0.00 0.00 0.06 0.03 0.24
## 164 0.00 0.00 0.00 0.00 0.27 0.00 0.07
## 165 0.49 0.00 0.00 0.00 0.26 0.02 0.29
## 166 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 167 0.01 0.00 0.00 0.00 0.20 0.00 0.02
## 168 0.08 0.00 0.00 0.00 0.00 0.00 0.00
## 169 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 170 0.00 0.00 0.00 0.00 0.15 0.03 0.13
## 171 0.04 0.00 0.00 0.00 0.02 0.01 0.00
## 172 0.33 0.00 0.00 0.00 0.04 0.16 0.05
## 173 0.03 0.00 0.00 0.00 0.00 0.00 0.01
## 174 0.00 0.00 0.00 0.00 0.05 0.00 0.00
## 175 0.01 0.00 0.00 0.00 0.01 0.00 0.00
## 176 0.01 0.00 0.00 0.00 0.03 0.00 0.00
## 177 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 178 0.04 0.00 0.00 0.00 0.21 0.06 0.10
## 179 0.01 0.00 0.00 0.00 0.07 0.01 0.05
## 180 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 181 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 182 0.00 0.00 0.00 0.00 0.01 0.00 0.02
## 183 0.67 0.00 0.00 0.00 0.29 0.06 0.09
## 184 0.07 0.00 0.00 0.00 0.43 0.07 0.20
## 185 0.05 0.00 0.00 0.00 0.08 0.00 0.02
## 186 0.01 0.00 0.00 0.00 0.53 0.04 0.22
## 187 0.18 0.00 0.00 0.00 0.48 0.05 0.23
## 188 0.07 0.00 0.00 0.00 0.01 0.00 0.00
## 189 0.18 0.00 0.00 0.00 0.15 0.01 0.05
## 190 0.00 0.00 0.00 0.00 0.01 0.00 0.00
## 191 0.03 0.00 0.00 0.00 0.12 0.00 0.01
## 192 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 193 0.16 0.00 0.00 0.00 0.03 0.00 0.00
## 194 0.33 0.00 0.00 0.00 0.07 0.01 0.12
## 195 0.00 0.00 0.00 0.00 0.16 0.00 0.17
## 196 0.00 0.00 0.00 0.00 0.01 0.00 0.00
## 197 0.01 0.00 0.00 0.00 0.54 0.01 0.09
## 198 0.18 0.00 0.00 0.00 0.12 0.01 0.17
## 3 0.01 0.00 0.00 0.00 0.32 0.00 0.24
## 4 0.00 0.00 0.00 0.00 0.02 0.00 0.01
## 11 0.00 0.00 0.00 0.00 0.04 0.00 0.23
## 12 0.00 0.00 0.00 0.00 0.16 0.00 0.50
## 13 0.00 0.00 0.00 0.00 0.06 0.00 0.06
## 14 0.00 0.00 0.00 0.00 0.08 0.00 0.22
## 15 0.00 0.00 0.00 0.00 0.47 0.00 0.19
## 16 0.00 0.00 0.00 0.00 0.00 0.00 0.09
## 17 0.00 0.00 0.00 0.00 0.00 0.00 0.26
## 18 0.00 0.00 0.00 0.00 0.03 0.00 0.11
## 19 0.00 0.00 0.00 0.00 0.00 0.00 0.01
## 20 0.00 0.00 0.00 0.00 0.29 0.00 0.11
## 21 0.00 0.00 0.00 0.00 0.04 0.00 0.73
## 22 0.02 0.00 0.00 0.00 0.17 0.01 0.44
## 23 0.00 0.00 0.00 0.00 0.22 0.00 0.13
## 24 0.00 0.00 0.00 0.00 0.03 0.00 0.07
## 25 0.00 0.00 0.00 0.00 0.00 0.00 0.30
## 26 0.01 0.00 0.00 0.00 0.58 0.01 0.78
## 27 0.00 0.00 0.00 0.00 0.00 0.00 0.04
## 28 0.00 0.00 0.00 0.00 0.00 0.00 0.03
## 29 0.00 0.00 0.00 0.00 0.39 0.01 0.64
## 30 0.00 0.00 0.00 0.00 0.00 0.00 0.06
## 31 0.00 0.00 0.00 0.00 0.00 0.00 0.09
## 32 0.01 0.00 0.00 0.00 0.07 0.00 0.43
## 33 0.02 0.00 0.00 0.00 0.17 0.00 0.38
## 34 0.00 0.00 0.00 0.00 0.26 0.00 0.42
## 35 0.00 0.00 0.00 0.00 0.01 0.00 0.04
## 36 0.00 0.00 0.00 0.00 0.00 0.00 0.16
## 37 0.00 0.00 0.00 0.00 0.00 0.00 0.01
## 38 0.00 0.00 0.00 0.00 0.62 0.01 0.50
## 39 0.00 0.00 0.00 0.00 0.00 0.00 0.02
## 40 0.01 0.00 0.00 0.00 0.26 0.00 0.16
## 41 0.00 0.00 0.00 0.00 0.07 0.00 0.20
## 42 0.00 0.00 0.00 0.00 0.00 0.00 0.19
## 43 0.00 0.00 0.00 0.00 0.05 0.00 0.22
## 44 0.00 0.00 0.00 0.00 0.31 0.00 0.60
## 45 0.00 0.00 0.00 0.00 0.00 0.00 0.02
## 46 0.00 0.00 0.00 0.00 0.00 0.00 0.18
## 47 0.00 0.00 0.00 0.00 0.02 0.00 0.09
## 48 0.00 0.00 0.00 0.00 0.00 0.00 0.06
## 49 0.05 0.00 0.00 0.00 0.34 0.02 0.92
## 50 0.03 0.00 0.00 0.00 0.68 0.03 0.82
## 51 0.00 0.00 0.00 0.00 0.01 0.00 0.05
## 52 0.00 0.00 0.00 0.00 0.01 0.00 0.03
## 53 0.00 0.00 0.00 0.00 0.07 0.00 0.30
## 54 0.08 0.00 0.00 0.00 0.12 0.00 0.55
## 55 0.00 0.00 0.00 0.00 0.00 0.00 0.03
## 56 0.00 0.00 0.00 0.00 0.15 0.00 0.31
## 57 0.00 0.00 0.00 0.00 0.41 0.00 0.52
## 1 0.00 0.00 0.00 0.00 0.00 0.12 0.10
## 199 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 200 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 201 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 202 0.00 0.00 0.00 0.00 0.01 0.05 0.01
## 203 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 204 0.00 0.00 0.00 0.00 0.01 0.01 0.00
## 205 0.00 0.00 0.00 0.00 0.08 0.01 0.02
## 206 0.00 0.00 0.00 0.00 0.08 0.46 0.04
## 207 0.00 0.00 0.00 0.00 0.02 0.05 0.02
## 208 0.03 0.00 0.00 0.00 0.39 0.61 0.23
## 209 0.00 0.00 0.00 0.00 0.04 0.32 0.11
## 210 0.00 0.00 0.00 0.00 0.03 0.61 0.20
## 211 0.07 0.00 0.00 0.00 0.12 0.61 0.31
## 212 0.00 0.00 0.00 0.00 0.01 0.05 0.02
## 213 0.00 0.00 0.00 0.00 0.01 0.01 0.00
## 214 0.00 0.00 0.00 0.00 0.00 0.02 0.01
## 215 0.00 0.00 0.00 0.00 0.01 0.03 0.00
## 216 0.01 0.00 0.00 0.00 0.00 0.33 0.00
## 217 0.00 0.00 0.00 0.00 0.02 0.02 0.02
## 218 0.00 0.00 0.00 0.00 0.00 0.16 0.01
## 219 0.00 0.00 0.00 0.00 0.01 0.03 0.01
## 220 0.01 0.00 0.00 0.00 0.03 0.02 0.00
## 221 0.00 0.00 0.00 0.00 0.10 0.08 0.01
## 222 0.01 0.00 0.00 0.00 0.01 0.07 0.01
## 223 0.06 0.00 0.00 0.00 0.04 0.17 0.04
## 224 0.46 0.00 0.00 0.00 0.21 0.02 0.21
## 225 0.06 0.00 0.00 0.00 0.00 0.00 0.00
## 226 0.01 0.00 0.00 0.00 0.01 0.00 0.00
## 227 0.09 0.00 0.00 0.00 0.00 0.00 0.01
## 228 0.02 0.00 0.00 0.00 0.02 0.00 0.02
## 229 0.03 0.00 0.00 0.00 0.01 0.00 0.02
## 230 0.54 0.00 0.00 0.00 0.17 0.00 0.55
## 231 0.71 0.00 0.00 0.00 0.17 0.02 0.45
## 232 0.06 0.00 0.00 0.00 0.00 0.00 0.04
## 233 0.29 0.00 0.00 0.00 0.00 0.00 0.00
## 234 0.03 0.00 0.00 0.00 0.03 0.00 0.08
## 235 0.05 0.00 0.00 0.00 0.02 0.00 0.32
## 236 0.86 0.00 0.00 0.00 0.06 0.07 0.17
## 237 0.44 0.00 0.00 0.00 0.17 0.60 0.44
## 238 0.75 0.00 0.00 0.00 0.39 0.05 0.21
## 239 0.24 0.00 0.00 0.00 0.00 0.00 0.01
## 240 0.57 0.00 0.00 0.00 0.61 0.18 0.08
## 243 0.02 0.00 0.00 0.00 0.09 0.66 0.43
## 248 0.07 0.00 0.00 0.00 0.16 0.15 0.06
## 249 0.11 0.00 0.00 0.00 0.01 0.02 0.00
## 250 0.78 0.00 0.00 0.00 0.02 0.01 0.01
## 251 0.31 0.00 0.01 0.00 0.01 0.25 0.03
## 252 0.16 0.00 0.00 0.00 0.00 0.02 0.00
## 253 0.02 0.00 0.00 0.00 0.02 0.00 0.00
## 254 0.09 0.00 0.01 0.00 0.01 0.09 0.00
## 263 0.13 0.00 0.00 0.00 0.03 0.02 0.12
## 268 0.30 0.00 0.53 0.00 0.00 0.11 0.00
## 269 0.02 0.00 0.03 0.00 0.02 0.90 0.08
## 270 0.01 0.00 0.01 0.00 0.00 0.10 0.00
## 271 0.00 0.10 0.51 0.00 0.00 0.02 0.00
## 272 0.11 0.00 0.00 0.00 0.01 0.01 0.00
## 273 0.00 0.00 0.00 0.00 0.01 0.02 0.00
## 274 0.00 0.00 0.00 0.00 0.01 0.09 0.02
## 275 0.00 0.00 0.00 0.00 0.00 0.13 0.00
## 276 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 290 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 291 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 292 0.01 0.00 0.00 0.00 0.00 0.00 0.00
## 293 0.40 0.00 0.01 0.00 0.02 0.10 0.03
## 294 0.63 0.00 0.00 0.00 0.10 0.05 0.03
## 265 0.02 0.00 0.07 0.00 0.00 0.01 0.00
## 266 0.00 0.00 0.02 0.10 0.00 0.00 0.00
## 404 0.00 0.00 0.09 0.00 0.00 0.00 0.00
## 405 0.00 0.02 0.07 0.00 0.00 0.00 0.00
## 406 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 407 0.00 0.00 0.01 0.00 0.00 0.00 0.00
## 408 0.00 0.01 0.02 0.00 0.00 0.00 0.00
## 409 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 410 0.00 0.00 0.01 0.00 0.00 0.00 0.00
## 416 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 417 0.00 0.00 0.02 0.00 0.00 0.00 0.00
## 418 0.00 0.09 0.78 0.00 0.00 0.00 0.00
## 419 0.00 0.03 0.01 0.00 0.00 0.00 0.00
## 420 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 241 0.01 0.04 0.12 0.00 0.00 0.01 0.00
## 242 0.00 0.02 0.02 0.00 0.00 0.00 0.00
## 244 0.10 0.00 0.00 0.00 0.03 0.08 0.03
## 245 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 246 0.00 0.00 0.00 0.00 0.00 0.00 0.00
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## 256 0.00 0.00 0.00 0.00 0.01 0.01 0.00
## 257 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 267 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 277 0.00 0.01 0.18 0.02 0.00 0.09 0.00
## 278 0.01 0.01 0.08 0.00 0.00 0.09 0.00
## 279 0.05 0.00 0.00 0.00 0.00 0.03 0.00
## 280 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 281 0.00 0.01 0.03 0.00 0.00 0.00 0.00
## 282 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 283 0.00 0.00 0.12 0.00 0.00 0.00 0.00
## 284 0.00 0.01 0.10 0.00 0.00 0.00 0.00
## 285 0.00 0.39 0.05 0.00 0.00 0.01 0.00
## 286 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 287 0.00 0.01 0.01 0.00 0.00 0.05 0.00
## 288 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 289 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 295 0.00 0.00 0.02 0.00 0.00 0.01 0.00
## 296 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 297 0.00 0.00 0.00 0.00 0.03 0.09 0.02
## 298 0.00 0.00 0.00 0.00 0.00 0.03 0.00
## 299 0.00 0.01 0.03 0.00 0.00 0.00 0.00
## 300 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 301 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 302 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 303 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 304 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 305 0.00 0.00 0.15 0.00 0.00 0.01 0.00
## 306 0.00 0.00 0.02 0.00 0.00 0.00 0.00
## 307 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 308 0.00 0.00 0.01 0.00 0.00 0.00 0.00
## 309 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 310 0.00 0.00 0.01 0.00 0.00 0.00 0.00
## 311 0.00 0.00 0.01 0.00 0.00 0.00 0.00
## 312 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 313 0.00 0.01 0.01 0.00 0.00 0.00 0.00
## 7 0.00 0.00 0.01 0.00 0.00 0.00 0.00
## 8 0.00 0.00 0.00 0.00 0.00 0.03 0.00
## 314 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 315 0.45 0.00 0.00 0.00 0.02 0.05 0.01
## 5 0.66 0.00 0.00 0.00 0.00 0.01 0.00
## 6 0.21 0.00 0.00 0.00 0.56 0.17 0.42
## 316 0.31 0.00 0.00 0.00 0.01 0.01 0.06
## 317 0.06 0.00 0.00 0.00 0.01 0.04 0.16
## 318 0.58 0.00 0.00 0.00 0.04 0.06 0.21
## 319 0.18 0.00 0.01 0.00 0.00 0.07 0.01
## 320 0.02 0.00 0.00 0.00 0.00 0.06 0.00
## 321 0.36 0.00 0.01 0.00 0.07 0.38 0.29
## 322 0.17 0.00 0.00 0.00 0.00 0.33 0.04
## 323 0.79 0.00 0.00 0.00 0.17 0.77 0.69
## 324 0.54 0.00 0.00 0.00 0.32 0.00 0.17
## 325 0.31 0.00 0.00 0.00 0.03 0.01 0.44
## 326 0.75 0.00 0.00 0.00 0.32 0.07 0.61
## 327 0.07 0.00 0.00 0.00 0.01 0.02 0.08
## 328 0.31 0.00 0.00 0.00 0.11 0.04 0.79
## 329 0.42 0.00 0.00 0.00 0.54 0.29 0.68
## 330 0.17 0.00 0.00 0.00 0.03 0.02 0.49
## 331 0.00 0.00 0.00 0.00 0.01 0.01 0.05
## 332 0.35 0.00 0.00 0.00 0.15 0.29 0.47
## 333 0.33 0.00 0.00 0.00 0.31 0.73 0.71
## 334 0.44 0.00 0.00 0.00 0.71 0.81 0.76
## 335 0.04 0.00 0.00 0.00 0.01 0.08 0.06
## 336 0.01 0.00 0.00 0.00 0.11 0.36 0.19
## 337 0.08 0.00 0.00 0.00 0.00 0.02 0.07
## 338 0.02 0.00 0.00 0.00 0.03 0.44 0.02
## 339 0.02 0.00 0.00 0.00 0.00 0.01 0.00
## 340 0.03 0.00 0.00 0.00 0.00 0.03 0.00
## 341 0.02 0.00 0.00 0.00 0.00 0.05 0.00
## 342 0.00 0.00 0.00 0.00 0.06 0.01 0.00
## 343 0.00 0.00 0.00 0.00 0.00 0.03 0.00
## 344 0.04 0.00 0.00 0.00 0.04 0.31 0.25
## 345 0.03 0.00 0.00 0.00 0.57 0.14 0.25
## 346 0.71 0.00 0.00 0.00 0.01 0.10 0.05
## 347 0.13 0.00 0.00 0.00 0.02 0.07 0.48
## 348 0.23 0.00 0.00 0.00 0.19 0.42 0.78
## 349 0.43 0.00 0.00 0.00 0.42 0.68 0.55
## 350 0.15 0.00 0.05 0.00 0.11 0.58 0.05
## 351 0.06 0.00 0.01 0.00 0.18 0.57 0.02
## 352 0.06 0.00 0.00 0.00 0.18 0.44 0.09
## 353 0.02 0.00 0.01 0.00 0.00 0.01 0.00
## 354 0.01 0.00 0.00 0.00 0.03 0.62 0.33
## 355 0.01 0.00 0.00 0.01 0.02 0.46 0.03
## 356 0.03 0.00 0.00 0.00 0.43 0.16 0.03
## 357 0.84 0.00 0.00 0.00 0.20 0.10 0.40
## 358 0.23 0.00 0.00 0.00 0.00 0.00 0.00
## 359 0.01 0.00 0.00 0.00 0.39 0.11 0.21
## 360 0.00 0.00 0.00 0.00 0.00 0.01 0.01
## 361 0.00 0.00 0.00 0.00 0.03 0.12 0.01
## 362 0.00 0.00 0.00 0.00 0.02 0.01 0.00
## 363 0.00 0.00 0.00 0.00 0.10 0.09 0.01
## 364 0.67 0.00 0.00 0.00 0.11 0.00 0.07
## 365 0.00 0.00 0.00 0.00 0.23 0.00 0.03
## 366 0.06 0.00 0.00 0.00 0.03 0.01 0.69
## 367 0.05 0.00 0.00 0.00 0.05 0.25 0.15
## 368 0.01 0.00 0.00 0.00 0.06 0.46 0.23
## 369 0.00 0.00 0.00 0.00 0.00 0.25 0.03
## 370 0.00 0.00 0.00 0.00 0.00 0.09 0.01
## 371 0.11 0.00 0.00 0.00 0.05 0.90 0.20
## 372 0.41 0.00 0.00 0.00 0.20 0.31 0.44
## 373 0.08 0.00 0.00 0.00 0.23 0.02 0.02
## 374 0.43 0.00 0.00 0.00 0.04 0.47 0.08
## 375 0.37 0.00 0.00 0.00 0.30 0.29 0.19
## 376 0.07 0.00 0.00 0.00 0.02 0.12 0.03
## 377 0.30 0.00 0.02 0.00 0.02 0.19 0.02
## 378 0.26 0.00 0.00 0.00 0.31 0.43 0.12
## 379 0.01 0.00 0.00 0.00 0.01 0.22 0.04
## 380 0.45 0.00 0.00 0.00 0.65 0.69 0.41
## 258 0.18 0.00 0.00 0.00 0.13 0.00 0.28
## 259 0.37 0.00 0.00 0.00 0.40 0.00 0.17
## 262 0.02 0.00 0.00 0.00 0.00 0.00 0.00
## 264 0.46 0.00 0.00 0.00 0.05 0.00 0.03
## 440 0.04 0.00 0.01 0.00 0.00 0.07 0.00
## 441 0.81 0.00 0.01 0.00 0.57 0.33 0.22
## 442 0.16 0.00 0.00 0.00 0.02 0.08 0.00
## 466 0.25 0.00 0.00 0.00 0.01 0.00 0.00
## 467 0.94 0.00 0.00 0.00 0.22 0.04 0.10
## 468 1.00 0.00 0.00 0.00 0.01 0.00 0.00
## 469 0.13 0.00 0.00 0.00 0.11 0.00 0.01
## 470 0.33 0.00 0.00 0.00 0.00 0.00 0.00
## 471 0.74 0.00 0.00 0.00 0.02 0.00 0.01
## 472 0.96 0.00 0.00 0.00 0.01 0.01 0.00
## 473 0.83 0.00 0.00 0.00 0.25 0.09 0.03
## 474 0.19 0.00 0.00 0.00 0.00 0.00 0.00
## 475 0.98 0.00 0.00 0.00 0.00 0.00 0.00
## 476 0.42 0.00 0.00 0.00 0.01 0.00 0.00
## 477 0.67 0.00 0.00 0.00 0.10 0.13 0.17
## 478 0.08 0.00 0.00 0.00 0.00 0.00 0.00
## 479 0.27 0.00 0.00 0.00 0.00 0.00 0.00
## 480 0.99 0.00 0.00 0.00 0.14 0.05 0.16
## 481 0.47 0.00 0.00 0.00 0.01 0.00 0.00
## 482 0.66 0.00 0.00 0.00 0.03 0.01 0.03
## 483 0.63 0.00 0.00 0.00 0.19 0.32 0.19
## 484 0.17 0.00 0.00 0.00 0.00 0.00 0.00
## 485 0.02 0.00 0.00 0.00 0.00 0.00 0.00
## 447 0.00 0.85 0.00 0.00 0.00 0.00 0.00
## 448 0.00 0.96 0.07 0.00 0.00 0.00 0.00
## 449 0.00 0.63 0.41 0.09 0.00 0.01 0.00
## 426 0.00 0.16 0.99 0.00 0.00 0.00 0.00
## 427 0.00 0.00 0.62 0.00 0.00 0.00 0.00
## 428 0.00 0.00 0.74 0.00 0.00 0.00 0.00
## 429 0.00 0.68 0.46 0.05 0.00 0.00 0.00
## 430 0.00 0.00 0.63 0.00 0.00 0.00 0.00
## 431 0.00 0.03 0.35 0.00 0.00 0.00 0.00
## 432 0.00 0.00 0.67 0.00 0.00 0.00 0.00
## 433 0.00 0.00 0.48 0.00 0.00 0.00 0.00
## 434 0.00 0.09 0.99 0.00 0.00 0.00 0.00
## 435 0.00 0.19 0.92 0.01 0.00 0.00 0.00
## 411 0.00 0.00 0.00 0.47 0.00 0.00 0.00
## 412 0.00 0.00 0.00 0.03 0.00 0.00 0.00
## 413 0.00 0.00 0.00 0.98 0.00 0.00 0.00
## 414 0.00 0.00 0.00 0.08 0.00 0.00 0.00
## 415 0.00 0.00 0.00 0.91 0.00 0.00 0.00
## 443 0.00 0.00 0.00 0.00 0.32 0.03 0.02
## 444 0.35 0.00 0.00 0.00 0.67 0.12 0.35
## 445 0.06 0.00 0.00 0.00 0.95 0.02 0.18
## 446 0.01 0.00 0.00 0.00 0.80 0.03 0.40
## 436 0.14 0.00 0.00 0.00 0.11 0.38 0.61
## 437 0.04 0.00 0.03 0.04 0.19 1.00 0.17
## 438 0.05 0.00 0.00 0.00 0.43 1.00 0.46
## 439 0.07 0.00 0.00 0.00 0.58 0.87 0.70
## 451 0.39 0.00 0.01 0.00 0.59 0.99 0.28
## 452 0.03 0.00 0.00 0.00 0.01 0.70 0.05
## 456 0.03 0.00 0.03 0.20 0.07 0.95 0.02
## 457 0.02 0.00 0.03 0.09 0.06 0.88 0.03
## 458 0.00 0.00 0.00 0.00 0.05 0.86 0.05
## 459 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## 460 0.00 0.00 0.00 0.00 0.00 0.88 0.01
## 461 0.03 0.00 0.00 0.00 0.03 0.82 0.18
## 462 0.00 0.00 0.00 0.00 0.05 0.70 0.08
## 463 0.00 0.00 0.00 0.00 0.11 0.62 0.12
## 464 0.00 0.00 0.00 0.00 0.03 0.86 0.10
## 465 0.00 0.00 0.00 0.00 0.03 0.74 0.02
## 421 0.04 0.00 0.00 0.00 0.23 0.08 0.88
## 422 0.02 0.00 0.00 0.00 0.07 0.31 0.68
## 423 0.01 0.00 0.00 0.00 0.30 0.02 0.90
## 424 0.00 0.00 0.00 0.00 0.20 0.15 0.88
## 425 0.24 0.00 0.00 0.00 0.85 0.93 0.99
## 453 0.19 0.00 0.00 0.00 0.91 0.07 0.98
## 454 0.01 0.00 0.00 0.00 0.16 0.01 0.67
## 455 0.00 0.00 0.00 0.00 0.29 0.17 0.984.2 cancellata
### 9 Classify specimen to species
cancellata <- shape_8[c(486), c(2:15)] #new specimen to classify
cancellata <- as.matrix(cancellata)
# get classification score
p.A <- cancellata%*%cj.A+cj0.A+log(All_singletons.lda$prior[1])
p.B <- cancellata%*%cj.B+cj0.B+log(All_singletons.lda$prior[2])
p.E <- cancellata%*%cj.E+cj0.E+log(All_singletons.lda$prior[3])
p.H1 <- cancellata%*%cj.H1+cj0.H1+log(All_singletons.lda$prior[4])
p.H2 <- cancellata%*%cj.H2+cj0.H2+log(All_singletons.lda$prior[5])
p.I <- cancellata%*%cj.I+cj0.I+log(All_singletons.lda$prior[6])
p.K <- cancellata%*%cj.K+cj0.K+log(All_singletons.lda$prior[7])
p.N <- cancellata%*%cj.N+cj0.N+log(All_singletons.lda$prior[8])
p.O <- cancellata%*%cj.O+cj0.O+log(All_singletons.lda$prior[9])
p.P <- cancellata%*%cj.P+cj0.P+log(All_singletons.lda$prior[10])
p.Q3 <- cancellata%*%cj.Q3+cj0.Q3+log(All_singletons.lda$prior[11])
p.Q4 <- cancellata%*%cj.Q4+cj0.Q4+log(All_singletons.lda$prior[12])
p.Q5 <- cancellata%*%cj.Q5+cj0.Q5+log(All_singletons.lda$prior[13])
p.R <- cancellata%*%cj.R+cj0.R+log(All_singletons.lda$prior[14])
p.S <- cancellata%*%cj.S+cj0.S+log(All_singletons.lda$prior[15])
p.weeksi <- cancellata%*%cj.weeksi+cj0.weeksi+log(All_singletons.lda$prior[16])
p.X2 <- cancellata%*%cj.X2+cj0.X2+log(All_singletons.lda$prior[17])
p.X3 <- cancellata%*%cj.X3+cj0.X3+log(All_singletons.lda$prior[18])
p.X4 <- cancellata%*%cj.X4+cj0.X4+log(All_singletons.lda$prior[19])
p.X5 <- cancellata%*%cj.X5+cj0.X5+log(All_singletons.lda$prior[20])
p.X8 <- cancellata%*%cj.X8+cj0.X8+log(All_singletons.lda$prior[21])
p.X9 <- cancellata%*%cj.X9+cj0.X9+log(All_singletons.lda$prior[22])
# posterior probability
post.A <- (exp(p.A-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.B <- (exp(p.B-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.E <- (exp(p.E-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H1 <- (exp(p.H1-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H2 <- (exp(p.H2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.I <- (exp(p.I-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.K <- (exp(p.K-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.N <- (exp(p.N-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.O <- (exp(p.O-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.P <- (exp(p.P-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q3 <- (exp(p.Q3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q4 <- (exp(p.Q4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q5 <- (exp(p.Q5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.R <- (exp(p.R-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.S <- (exp(p.S-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.weeksi <- (exp(p.weeksi-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X2 <- (exp(p.X2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X3 <- (exp(p.X3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X4 <- (exp(p.X4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X5 <- (exp(p.X5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X8 <- (exp(p.X8-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X9 <- (exp(p.X9-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
cancellata.posteriors <- as.matrix(c(post.A, post.B, post.E, post.H1, post.H2, post.I, post.K, post.N, post.O, post.P, post.Q3, post.Q4, post.Q5, post.R, post.S, post.weeksi, post.X2, post.X3, post.X4, post.X5, post.X8, post.X9))
rownames(cancellata.posteriors) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(cancellata.posteriors) <- "cancellata.posteriors"
cancellata.posteriors <- round(cancellata.posteriors, digits=3)
cancellata.posteriors## cancellata.posteriors
## A 0.000
## B 0.000
## E 0.000
## H1 0.001
## H2 0.007
## I 0.000
## K 0.002
## N 0.004
## O 0.902
## P 0.002
## Q3 0.000
## Q4 0.000
## Q5 0.000
## R 0.000
## S 0.079
## weeksi 0.000
## X2 0.000
## X3 0.000
## X4 0.000
## X5 0.001
## X8 0.002
## X9 0.000#typicality probability
d2.A <- (t(as.vector(cancellata)-mean.A))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.A)
typicality.A <- pchisq(d2.A, df = 14, lower.tail = FALSE)
d2.B <- (t(as.vector(cancellata)-mean.B))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.B)
typicality.B <- pchisq(d2.B, df = 14, lower.tail = FALSE)
d2.E <- (t(as.vector(cancellata)-mean.E))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.E)
typicality.E <- pchisq(d2.E, df = 14, lower.tail = FALSE)
d2.H1 <- (t(as.vector(cancellata)-mean.H1))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.H1)
typicality.H1 <- pchisq(d2.H1, df = 14, lower.tail = FALSE)
d2.H2 <- (t(as.vector(cancellata)-mean.H2))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.H2)
typicality.H2 <- pchisq(d2.H2, df = 14, lower.tail = FALSE)
d2.I <- (t(as.vector(cancellata)-mean.I))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.I)
typicality.I <- pchisq(d2.I, df = 14, lower.tail = FALSE)
d2.K <- (t(as.vector(cancellata)-mean.K))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.K)
typicality.K <- pchisq(d2.K, df = 14, lower.tail = FALSE)
d2.N <- (t(as.vector(cancellata)-mean.N))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.N)
typicality.N <- pchisq(d2.N, df = 14, lower.tail = FALSE)
d2.O <- (t(as.vector(cancellata)-mean.O))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.O)
typicality.O <- pchisq(d2.O, df = 14, lower.tail = FALSE)
d2.P <- (t(as.vector(cancellata)-mean.P))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.P)
typicality.P <- pchisq(d2.P, df = 14, lower.tail = FALSE)
d2.Q3 <- (t(as.vector(cancellata)-mean.Q3))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.Q3)
typicality.Q3 <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q4 <- (t(as.vector(cancellata)-mean.Q4))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.Q4)
typicality.Q4 <- pchisq(d2.Q4, df = 14, lower.tail = FALSE)
d2.Q5 <- (t(as.vector(cancellata)-mean.Q5))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.Q5)
typicality.Q5 <- pchisq(d2.Q5, df = 14, lower.tail = FALSE)
d2.R <- (t(as.vector(cancellata)-mean.R))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.R)
typicality.R <- pchisq(d2.R, df = 14, lower.tail = FALSE)
d2.S <- (t(as.vector(cancellata)-mean.S))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.S)
typicality.S <- pchisq(d2.S, df = 14, lower.tail = FALSE)
d2.weeksi <- (t(as.vector(cancellata)-mean.weeksi))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.weeksi)
typicality.weeksi <- pchisq(d2.weeksi, df = 14, lower.tail = FALSE)
d2.X2 <- (t(as.vector(cancellata)-mean.X2))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.X2)
typicality.X2 <- pchisq(d2.X2, df = 14, lower.tail = FALSE)
d2.X3 <- (t(as.vector(cancellata)-mean.X3))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.X3)
typicality.X3 <- pchisq(d2.X3, df = 14, lower.tail = FALSE)
d2.X4 <- (t(as.vector(cancellata)-mean.X4))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.X4)
typicality.X4 <- pchisq(d2.X4, df = 14, lower.tail = FALSE)
d2.X5 <- (t(as.vector(cancellata)-mean.X5))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.X5)
typicality.X5 <- pchisq(d2.X5, df = 14, lower.tail = FALSE)
d2.X8 <- (t(as.vector(cancellata)-mean.X8))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.X8)
typicality.X8 <- pchisq(d2.X8, df = 14, lower.tail = FALSE)
d2.X9 <- (t(as.vector(cancellata)-mean.X9))%*%solve(cov.d)%*%(as.vector(cancellata)-mean.X9)
typicality.X9 <- pchisq(d2.X9, df = 14, lower.tail = FALSE)
cancellata.typicality <- as.matrix(c(typicality.A, typicality.B, typicality.E, typicality.H1, typicality.H2, typicality.I, typicality.K, typicality.N, typicality.O, typicality.P, typicality.Q3, typicality.Q4, typicality.Q5, typicality.R, typicality.S, typicality.weeksi, typicality.X2, typicality.X3, typicality.X4, typicality.X5, typicality.X8, typicality.X9))
rownames(cancellata.typicality) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(cancellata.typicality) <- "cancellata.typicality"
cancellata.typicality <- round(cancellata.typicality, digits=6)
cancellata.typicality## cancellata.typicality
## A 0.000000
## B 0.000001
## E 0.000000
## H1 0.004144
## H2 0.044584
## I 0.002952
## K 0.005147
## N 0.008606
## O 0.223405
## P 0.009827
## Q3 0.000028
## Q4 0.000000
## Q5 0.000000
## R 0.000003
## S 0.040988
## weeksi 0.000413
## X2 0.000000
## X3 0.000000
## X4 0.000000
## X5 0.013969
## X8 0.009213
## X9 0.005408The drawing of Ozestheria cancellata can be assigned to O (90.2%) or S (7.9%).
4.3 minor J54045a
### 9 Classify specimen to species
minor <- shape_8[c(487), c(2:15)] #new specimen to classify
minor <- as.matrix(minor)
# get classification score
p.A <- minor%*%cj.A+cj0.A+log(All_singletons.lda$prior[1])
p.B <- minor%*%cj.B+cj0.B+log(All_singletons.lda$prior[2])
p.E <- minor%*%cj.E+cj0.E+log(All_singletons.lda$prior[3])
p.H1 <- minor%*%cj.H1+cj0.H1+log(All_singletons.lda$prior[4])
p.H2 <- minor%*%cj.H2+cj0.H2+log(All_singletons.lda$prior[5])
p.I <- minor%*%cj.I+cj0.I+log(All_singletons.lda$prior[6])
p.K <- minor%*%cj.K+cj0.K+log(All_singletons.lda$prior[7])
p.N <- minor%*%cj.N+cj0.N+log(All_singletons.lda$prior[8])
p.O <- minor%*%cj.O+cj0.O+log(All_singletons.lda$prior[9])
p.P <- minor%*%cj.P+cj0.P+log(All_singletons.lda$prior[10])
p.Q3 <- minor%*%cj.Q3+cj0.Q3+log(All_singletons.lda$prior[11])
p.Q4 <- minor%*%cj.Q4+cj0.Q4+log(All_singletons.lda$prior[12])
p.Q5 <- minor%*%cj.Q5+cj0.Q5+log(All_singletons.lda$prior[13])
p.R <- minor%*%cj.R+cj0.R+log(All_singletons.lda$prior[14])
p.S <- minor%*%cj.S+cj0.S+log(All_singletons.lda$prior[15])
p.weeksi <- minor%*%cj.weeksi+cj0.weeksi+log(All_singletons.lda$prior[16])
p.X2 <- minor%*%cj.X2+cj0.X2+log(All_singletons.lda$prior[17])
p.X3 <- minor%*%cj.X3+cj0.X3+log(All_singletons.lda$prior[18])
p.X4 <- minor%*%cj.X4+cj0.X4+log(All_singletons.lda$prior[19])
p.X5 <- minor%*%cj.X5+cj0.X5+log(All_singletons.lda$prior[20])
p.X8 <- minor%*%cj.X8+cj0.X8+log(All_singletons.lda$prior[21])
p.X9 <- minor%*%cj.X9+cj0.X9+log(All_singletons.lda$prior[22])
# posterior probability
post.A <- (exp(p.A-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.B <- (exp(p.B-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.E <- (exp(p.E-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H1 <- (exp(p.H1-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H2 <- (exp(p.H2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.I <- (exp(p.I-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.K <- (exp(p.K-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.N <- (exp(p.N-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.O <- (exp(p.O-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.P <- (exp(p.P-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q3 <- (exp(p.Q3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q4 <- (exp(p.Q4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q5 <- (exp(p.Q5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.R <- (exp(p.R-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.S <- (exp(p.S-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.weeksi <- (exp(p.weeksi-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X2 <- (exp(p.X2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X3 <- (exp(p.X3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X4 <- (exp(p.X4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X5 <- (exp(p.X5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X8 <- (exp(p.X8-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X9 <- (exp(p.X9-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
minor.posteriors <- as.matrix(c(post.A, post.B, post.E, post.H1, post.H2, post.I, post.K, post.N, post.O, post.P, post.Q3, post.Q4, post.Q5, post.R, post.S, post.weeksi, post.X2, post.X3, post.X4, post.X5, post.X8, post.X9))
rownames(minor.posteriors) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(minor.posteriors) <- "minor.posteriors"
minor.posteriors <- round(minor.posteriors, digits=3)
minor.posteriors## minor.posteriors
## A 0.009
## B 0.912
## E 0.036
## H1 0.000
## H2 0.000
## I 0.001
## K 0.000
## N 0.000
## O 0.002
## P 0.003
## Q3 0.007
## Q4 0.000
## Q5 0.000
## R 0.000
## S 0.028
## weeksi 0.000
## X2 0.000
## X3 0.000
## X4 0.000
## X5 0.000
## X8 0.001
## X9 0.000#typicality probability
d2.A <- (t(as.vector(minor)-mean.A))%*%solve(cov.d)%*%(as.vector(minor)-mean.A)
typicality.A <- pchisq(d2.A, df = 14, lower.tail = FALSE)
d2.B <- (t(as.vector(minor)-mean.B))%*%solve(cov.d)%*%(as.vector(minor)-mean.B)
typicality.B <- pchisq(d2.B, df = 14, lower.tail = FALSE)
d2.E <- (t(as.vector(minor)-mean.E))%*%solve(cov.d)%*%(as.vector(minor)-mean.E)
typicality.E <- pchisq(d2.E, df = 14, lower.tail = FALSE)
d2.H1 <- (t(as.vector(minor)-mean.H1))%*%solve(cov.d)%*%(as.vector(minor)-mean.H1)
typicality.H1 <- pchisq(d2.H1, df = 14, lower.tail = FALSE)
d2.H2 <- (t(as.vector(minor)-mean.H2))%*%solve(cov.d)%*%(as.vector(minor)-mean.H2)
typicality.H2 <- pchisq(d2.H2, df = 14, lower.tail = FALSE)
d2.I <- (t(as.vector(minor)-mean.I))%*%solve(cov.d)%*%(as.vector(minor)-mean.I)
typicality.I <- pchisq(d2.I, df = 14, lower.tail = FALSE)
d2.K <- (t(as.vector(minor)-mean.K))%*%solve(cov.d)%*%(as.vector(minor)-mean.K)
typicality.K <- pchisq(d2.K, df = 14, lower.tail = FALSE)
d2.N <- (t(as.vector(minor)-mean.N))%*%solve(cov.d)%*%(as.vector(minor)-mean.N)
typicality.N <- pchisq(d2.N, df = 14, lower.tail = FALSE)
d2.O <- (t(as.vector(minor)-mean.O))%*%solve(cov.d)%*%(as.vector(minor)-mean.O)
typicality.O <- pchisq(d2.O, df = 14, lower.tail = FALSE)
d2.P <- (t(as.vector(minor)-mean.P))%*%solve(cov.d)%*%(as.vector(minor)-mean.P)
typicality.P <- pchisq(d2.P, df = 14, lower.tail = FALSE)
d2.Q3 <- (t(as.vector(minor)-mean.Q3))%*%solve(cov.d)%*%(as.vector(minor)-mean.Q3)
typicality.Q3 <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q4 <- (t(as.vector(minor)-mean.Q4))%*%solve(cov.d)%*%(as.vector(minor)-mean.Q4)
typicality.Q4 <- pchisq(d2.Q4, df = 14, lower.tail = FALSE)
d2.Q5 <- (t(as.vector(minor)-mean.Q5))%*%solve(cov.d)%*%(as.vector(minor)-mean.Q5)
typicality.Q5 <- pchisq(d2.Q5, df = 14, lower.tail = FALSE)
d2.R <- (t(as.vector(minor)-mean.R))%*%solve(cov.d)%*%(as.vector(minor)-mean.R)
typicality.R <- pchisq(d2.R, df = 14, lower.tail = FALSE)
d2.S <- (t(as.vector(minor)-mean.S))%*%solve(cov.d)%*%(as.vector(minor)-mean.S)
typicality.S <- pchisq(d2.S, df = 14, lower.tail = FALSE)
d2.weeksi <- (t(as.vector(minor)-mean.weeksi))%*%solve(cov.d)%*%(as.vector(minor)-mean.weeksi)
typicality.weeksi <- pchisq(d2.weeksi, df = 14, lower.tail = FALSE)
d2.X2 <- (t(as.vector(minor)-mean.X2))%*%solve(cov.d)%*%(as.vector(minor)-mean.X2)
typicality.X2 <- pchisq(d2.X2, df = 14, lower.tail = FALSE)
d2.X3 <- (t(as.vector(minor)-mean.X3))%*%solve(cov.d)%*%(as.vector(minor)-mean.X3)
typicality.X3 <- pchisq(d2.X3, df = 14, lower.tail = FALSE)
d2.X4 <- (t(as.vector(minor)-mean.X4))%*%solve(cov.d)%*%(as.vector(minor)-mean.X4)
typicality.X4 <- pchisq(d2.X4, df = 14, lower.tail = FALSE)
d2.X5 <- (t(as.vector(minor)-mean.X5))%*%solve(cov.d)%*%(as.vector(minor)-mean.X5)
typicality.X5 <- pchisq(d2.X5, df = 14, lower.tail = FALSE)
d2.X8 <- (t(as.vector(minor)-mean.X8))%*%solve(cov.d)%*%(as.vector(minor)-mean.X8)
typicality.X8 <- pchisq(d2.X8, df = 14, lower.tail = FALSE)
d2.X9 <- (t(as.vector(minor)-mean.X9))%*%solve(cov.d)%*%(as.vector(minor)-mean.X9)
typicality.X9 <- pchisq(d2.X9, df = 14, lower.tail = FALSE)
minor.typicality <- as.matrix(c(typicality.A, typicality.B, typicality.E, typicality.H1, typicality.H2, typicality.I, typicality.K, typicality.N, typicality.O, typicality.P, typicality.Q3, typicality.Q4, typicality.Q5, typicality.R, typicality.S, typicality.weeksi, typicality.X2, typicality.X3, typicality.X4, typicality.X5, typicality.X8, typicality.X9))
rownames(minor.typicality) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(minor.typicality) <- "minor.typicality"
minor.typicality <- round(minor.typicality, digits=6)
minor.typicality## minor.typicality
## A 0.195540
## B 0.819668
## E 0.483795
## H1 0.036887
## H2 0.049726
## I 0.171635
## K 0.023968
## N 0.000369
## O 0.088090
## P 0.149694
## Q3 0.186853
## Q4 0.003219
## Q5 0.016877
## R 0.025732
## S 0.221517
## weeksi 0.027896
## X2 0.000005
## X3 0.000168
## X4 0.000000
## X5 0.061969
## X8 0.109543
## X9 0.045336Specimen J54045a (Ozestheria minor) can be assigned to B (91.2%), B also receives the highest typicality score.
4.4 minor_mean
### 9 Classify specimen to species
minor_mean <- colMeans(shape_8[c(487:491), c(2:15)]) #new specimen to classify
#minor_mean <- as.matrix(minor_mean)
# get classification score
p.A <- minor_mean%*%cj.A+cj0.A+log(All_singletons.lda$prior[1])
p.B <- minor_mean%*%cj.B+cj0.B+log(All_singletons.lda$prior[2])
p.E <- minor_mean%*%cj.E+cj0.E+log(All_singletons.lda$prior[3])
p.H1 <- minor_mean%*%cj.H1+cj0.H1+log(All_singletons.lda$prior[4])
p.H2 <- minor_mean%*%cj.H2+cj0.H2+log(All_singletons.lda$prior[5])
p.I <- minor_mean%*%cj.I+cj0.I+log(All_singletons.lda$prior[6])
p.K <- minor_mean%*%cj.K+cj0.K+log(All_singletons.lda$prior[7])
p.N <- minor_mean%*%cj.N+cj0.N+log(All_singletons.lda$prior[8])
p.O <- minor_mean%*%cj.O+cj0.O+log(All_singletons.lda$prior[9])
p.P <- minor_mean%*%cj.P+cj0.P+log(All_singletons.lda$prior[10])
p.Q3 <- minor_mean%*%cj.Q3+cj0.Q3+log(All_singletons.lda$prior[11])
p.Q4 <- minor_mean%*%cj.Q4+cj0.Q4+log(All_singletons.lda$prior[12])
p.Q5 <- minor_mean%*%cj.Q5+cj0.Q5+log(All_singletons.lda$prior[13])
p.R <- minor_mean%*%cj.R+cj0.R+log(All_singletons.lda$prior[14])
p.S <- minor_mean%*%cj.S+cj0.S+log(All_singletons.lda$prior[15])
p.weeksi <- minor_mean%*%cj.weeksi+cj0.weeksi+log(All_singletons.lda$prior[16])
p.X2 <- minor_mean%*%cj.X2+cj0.X2+log(All_singletons.lda$prior[17])
p.X3 <- minor_mean%*%cj.X3+cj0.X3+log(All_singletons.lda$prior[18])
p.X4 <- minor_mean%*%cj.X4+cj0.X4+log(All_singletons.lda$prior[19])
p.X5 <- minor_mean%*%cj.X5+cj0.X5+log(All_singletons.lda$prior[20])
p.X8 <- minor_mean%*%cj.X8+cj0.X8+log(All_singletons.lda$prior[21])
p.X9 <- minor_mean%*%cj.X9+cj0.X9+log(All_singletons.lda$prior[22])
# posterior probability
post.A <- (exp(p.A-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.B <- (exp(p.B-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.E <- (exp(p.E-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H1 <- (exp(p.H1-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H2 <- (exp(p.H2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.I <- (exp(p.I-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.K <- (exp(p.K-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.N <- (exp(p.N-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.O <- (exp(p.O-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.P <- (exp(p.P-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q3 <- (exp(p.Q3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q4 <- (exp(p.Q4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q5 <- (exp(p.Q5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.R <- (exp(p.R-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.S <- (exp(p.S-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.weeksi <- (exp(p.weeksi-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X2 <- (exp(p.X2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X3 <- (exp(p.X3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X4 <- (exp(p.X4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X5 <- (exp(p.X5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X8 <- (exp(p.X8-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X9 <- (exp(p.X9-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
minor_mean.posteriors <- as.matrix(c(post.A, post.B, post.E, post.H1, post.H2, post.I, post.K, post.N, post.O, post.P, post.Q3, post.Q4, post.Q5, post.R, post.S, post.weeksi, post.X2, post.X3, post.X4, post.X5, post.X8, post.X9))
rownames(minor_mean.posteriors) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(minor_mean.posteriors) <- "minor_mean.posteriors"
minor_mean.posteriors <- round(minor_mean.posteriors, digits=3)
minor_mean.posteriors## minor_mean.posteriors
## A 0.004
## B 0.137
## E 0.006
## H1 0.010
## H2 0.083
## I 0.011
## K 0.006
## N 0.000
## O 0.190
## P 0.006
## Q3 0.028
## Q4 0.000
## Q5 0.000
## R 0.001
## S 0.429
## weeksi 0.000
## X2 0.000
## X3 0.000
## X4 0.000
## X5 0.008
## X8 0.075
## X9 0.006#typicality probability
d2.A <- (t(as.vector(minor_mean)-mean.A))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.A)
typicality.A <- pchisq(d2.A, df = 14, lower.tail = FALSE)
d2.B <- (t(as.vector(minor_mean)-mean.B))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.B)
typicality.B <- pchisq(d2.B, df = 14, lower.tail = FALSE)
d2.E <- (t(as.vector(minor_mean)-mean.E))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.E)
typicality.E <- pchisq(d2.E, df = 14, lower.tail = FALSE)
d2.H1 <- (t(as.vector(minor_mean)-mean.H1))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.H1)
typicality.H1 <- pchisq(d2.H1, df = 14, lower.tail = FALSE)
d2.H2 <- (t(as.vector(minor_mean)-mean.H2))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.H2)
typicality.H2 <- pchisq(d2.H2, df = 14, lower.tail = FALSE)
d2.I <- (t(as.vector(minor_mean)-mean.I))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.I)
typicality.I <- pchisq(d2.I, df = 14, lower.tail = FALSE)
d2.K <- (t(as.vector(minor_mean)-mean.K))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.K)
typicality.K <- pchisq(d2.K, df = 14, lower.tail = FALSE)
d2.N <- (t(as.vector(minor_mean)-mean.N))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.N)
typicality.N <- pchisq(d2.N, df = 14, lower.tail = FALSE)
d2.O <- (t(as.vector(minor_mean)-mean.O))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.O)
typicality.O <- pchisq(d2.O, df = 14, lower.tail = FALSE)
d2.P <- (t(as.vector(minor_mean)-mean.P))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.P)
typicality.P <- pchisq(d2.P, df = 14, lower.tail = FALSE)
d2.Q3 <- (t(as.vector(minor_mean)-mean.Q3))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.Q3)
typicality.Q3 <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q4 <- (t(as.vector(minor_mean)-mean.Q4))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.Q4)
typicality.Q4 <- pchisq(d2.Q4, df = 14, lower.tail = FALSE)
d2.Q5 <- (t(as.vector(minor_mean)-mean.Q5))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.Q5)
typicality.Q5 <- pchisq(d2.Q5, df = 14, lower.tail = FALSE)
d2.R <- (t(as.vector(minor_mean)-mean.R))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.R)
typicality.R <- pchisq(d2.R, df = 14, lower.tail = FALSE)
d2.S <- (t(as.vector(minor_mean)-mean.S))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.S)
typicality.S <- pchisq(d2.S, df = 14, lower.tail = FALSE)
d2.weeksi <- (t(as.vector(minor_mean)-mean.weeksi))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.weeksi)
typicality.weeksi <- pchisq(d2.weeksi, df = 14, lower.tail = FALSE)
d2.X2 <- (t(as.vector(minor_mean)-mean.X2))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.X2)
typicality.X2 <- pchisq(d2.X2, df = 14, lower.tail = FALSE)
d2.X3 <- (t(as.vector(minor_mean)-mean.X3))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.X3)
typicality.X3 <- pchisq(d2.X3, df = 14, lower.tail = FALSE)
d2.X4 <- (t(as.vector(minor_mean)-mean.X4))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.X4)
typicality.X4 <- pchisq(d2.X4, df = 14, lower.tail = FALSE)
d2.X5 <- (t(as.vector(minor_mean)-mean.X5))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.X5)
typicality.X5 <- pchisq(d2.X5, df = 14, lower.tail = FALSE)
d2.X8 <- (t(as.vector(minor_mean)-mean.X8))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.X8)
typicality.X8 <- pchisq(d2.X8, df = 14, lower.tail = FALSE)
d2.X9 <- (t(as.vector(minor_mean)-mean.X9))%*%solve(cov.d)%*%(as.vector(minor_mean)-mean.X9)
typicality.X9 <- pchisq(d2.X9, df = 14, lower.tail = FALSE)
minor_mean.typicality <- as.matrix(c(typicality.A, typicality.B, typicality.E, typicality.H1, typicality.H2, typicality.I, typicality.K, typicality.N, typicality.O, typicality.P, typicality.Q3, typicality.Q4, typicality.Q5, typicality.R, typicality.S, typicality.weeksi, typicality.X2, typicality.X3, typicality.X4, typicality.X5, typicality.X8, typicality.X9))
rownames(minor_mean.typicality) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(minor_mean.typicality) <- "minor_mean.typicality"
minor_mean.typicality <- round(minor_mean.typicality, digits=6)
minor_mean.typicality## minor_mean.typicality
## A 0.134366
## B 0.519736
## E 0.239716
## H1 0.238502
## H2 0.729531
## I 0.448356
## K 0.106440
## N 0.016568
## O 0.600102
## P 0.193861
## Q3 0.336633
## Q4 0.000359
## Q5 0.018379
## R 0.140938
## S 0.578957
## weeksi 0.012234
## X2 0.000000
## X3 0.000170
## X4 0.000000
## X5 0.400020
## X8 0.529019
## X9 0.279287The mean shape of the five Ozestheria minor type specimens can be assigned to one of five lineages: S (42.9%), O (19.0%), B (13.7%), H2 (8.3%), or X8 (7.5%). In order to assign a lineage to O. minor, further carapace details of these five lineages must be compared with O. minor. Ozestheria minor receives overall high typicality scores, indicating that the carapace shape of O. minor is typical of the condyle_long Ozestheria morphospace.
4.5 typica J54046a
### 9 Classify specimen to species
typica <- shape_8[c(492), c(2:15)] #new specimen to classify
typica <- as.matrix(typica)
# get classification score
p.A <- typica%*%cj.A+cj0.A+log(All_singletons.lda$prior[1])
p.B <- typica%*%cj.B+cj0.B+log(All_singletons.lda$prior[2])
p.E <- typica%*%cj.E+cj0.E+log(All_singletons.lda$prior[3])
p.H1 <- typica%*%cj.H1+cj0.H1+log(All_singletons.lda$prior[4])
p.H2 <- typica%*%cj.H2+cj0.H2+log(All_singletons.lda$prior[5])
p.I <- typica%*%cj.I+cj0.I+log(All_singletons.lda$prior[6])
p.K <- typica%*%cj.K+cj0.K+log(All_singletons.lda$prior[7])
p.N <- typica%*%cj.N+cj0.N+log(All_singletons.lda$prior[8])
p.O <- typica%*%cj.O+cj0.O+log(All_singletons.lda$prior[9])
p.P <- typica%*%cj.P+cj0.P+log(All_singletons.lda$prior[10])
p.Q3 <- typica%*%cj.Q3+cj0.Q3+log(All_singletons.lda$prior[11])
p.Q4 <- typica%*%cj.Q4+cj0.Q4+log(All_singletons.lda$prior[12])
p.Q5 <- typica%*%cj.Q5+cj0.Q5+log(All_singletons.lda$prior[13])
p.R <- typica%*%cj.R+cj0.R+log(All_singletons.lda$prior[14])
p.S <- typica%*%cj.S+cj0.S+log(All_singletons.lda$prior[15])
p.weeksi <- typica%*%cj.weeksi+cj0.weeksi+log(All_singletons.lda$prior[16])
p.X2 <- typica%*%cj.X2+cj0.X2+log(All_singletons.lda$prior[17])
p.X3 <- typica%*%cj.X3+cj0.X3+log(All_singletons.lda$prior[18])
p.X4 <- typica%*%cj.X4+cj0.X4+log(All_singletons.lda$prior[19])
p.X5 <- typica%*%cj.X5+cj0.X5+log(All_singletons.lda$prior[20])
p.X8 <- typica%*%cj.X8+cj0.X8+log(All_singletons.lda$prior[21])
p.X9 <- typica%*%cj.X9+cj0.X9+log(All_singletons.lda$prior[22])
# posterior probability
post.A <- (exp(p.A-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.B <- (exp(p.B-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.E <- (exp(p.E-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H1 <- (exp(p.H1-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H2 <- (exp(p.H2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.I <- (exp(p.I-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.K <- (exp(p.K-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.N <- (exp(p.N-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.O <- (exp(p.O-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.P <- (exp(p.P-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q3 <- (exp(p.Q3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q4 <- (exp(p.Q4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q5 <- (exp(p.Q5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.R <- (exp(p.R-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.S <- (exp(p.S-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.weeksi <- (exp(p.weeksi-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X2 <- (exp(p.X2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X3 <- (exp(p.X3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X4 <- (exp(p.X4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X5 <- (exp(p.X5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X8 <- (exp(p.X8-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X9 <- (exp(p.X9-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
typica.posteriors <- as.matrix(c(post.A, post.B, post.E, post.H1, post.H2, post.I, post.K, post.N, post.O, post.P, post.Q3, post.Q4, post.Q5, post.R, post.S, post.weeksi, post.X2, post.X3, post.X4, post.X5, post.X8, post.X9))
rownames(typica.posteriors) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(typica.posteriors) <- "typica.posteriors"
typica.posteriors <- round(typica.posteriors, digits=3)
typica.posteriors## typica.posteriors
## A 0.000
## B 0.000
## E 0.000
## H1 0.000
## H2 0.000
## I 0.000
## K 0.346
## N 0.000
## O 0.000
## P 0.003
## Q3 0.185
## Q4 0.030
## Q5 0.319
## R 0.113
## S 0.000
## weeksi 0.004
## X2 0.000
## X3 0.000
## X4 0.000
## X5 0.000
## X8 0.000
## X9 0.000#typicality probability
d2.A <- (t(as.vector(typica)-mean.A))%*%solve(cov.d)%*%(as.vector(typica)-mean.A)
typicality.A <- pchisq(d2.A, df = 14, lower.tail = FALSE)
d2.B <- (t(as.vector(typica)-mean.B))%*%solve(cov.d)%*%(as.vector(typica)-mean.B)
typicality.B <- pchisq(d2.B, df = 14, lower.tail = FALSE)
d2.E <- (t(as.vector(typica)-mean.E))%*%solve(cov.d)%*%(as.vector(typica)-mean.E)
typicality.E <- pchisq(d2.E, df = 14, lower.tail = FALSE)
d2.H1 <- (t(as.vector(typica)-mean.H1))%*%solve(cov.d)%*%(as.vector(typica)-mean.H1)
typicality.H1 <- pchisq(d2.H1, df = 14, lower.tail = FALSE)
d2.H2 <- (t(as.vector(typica)-mean.H2))%*%solve(cov.d)%*%(as.vector(typica)-mean.H2)
typicality.H2 <- pchisq(d2.H2, df = 14, lower.tail = FALSE)
d2.I <- (t(as.vector(typica)-mean.I))%*%solve(cov.d)%*%(as.vector(typica)-mean.I)
typicality.I <- pchisq(d2.I, df = 14, lower.tail = FALSE)
d2.K <- (t(as.vector(typica)-mean.K))%*%solve(cov.d)%*%(as.vector(typica)-mean.K)
typicality.K <- pchisq(d2.K, df = 14, lower.tail = FALSE)
d2.N <- (t(as.vector(typica)-mean.N))%*%solve(cov.d)%*%(as.vector(typica)-mean.N)
typicality.N <- pchisq(d2.N, df = 14, lower.tail = FALSE)
d2.O <- (t(as.vector(typica)-mean.O))%*%solve(cov.d)%*%(as.vector(typica)-mean.O)
typicality.O <- pchisq(d2.O, df = 14, lower.tail = FALSE)
d2.P <- (t(as.vector(typica)-mean.P))%*%solve(cov.d)%*%(as.vector(typica)-mean.P)
typicality.P <- pchisq(d2.P, df = 14, lower.tail = FALSE)
d2.Q3 <- (t(as.vector(typica)-mean.Q3))%*%solve(cov.d)%*%(as.vector(typica)-mean.Q3)
typicality.Q3 <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q4 <- (t(as.vector(typica)-mean.Q4))%*%solve(cov.d)%*%(as.vector(typica)-mean.Q4)
typicality.Q4 <- pchisq(d2.Q4, df = 14, lower.tail = FALSE)
d2.Q5 <- (t(as.vector(typica)-mean.Q5))%*%solve(cov.d)%*%(as.vector(typica)-mean.Q5)
typicality.Q5 <- pchisq(d2.Q5, df = 14, lower.tail = FALSE)
d2.R <- (t(as.vector(typica)-mean.R))%*%solve(cov.d)%*%(as.vector(typica)-mean.R)
typicality.R <- pchisq(d2.R, df = 14, lower.tail = FALSE)
d2.S <- (t(as.vector(typica)-mean.S))%*%solve(cov.d)%*%(as.vector(typica)-mean.S)
typicality.S <- pchisq(d2.S, df = 14, lower.tail = FALSE)
d2.weeksi <- (t(as.vector(typica)-mean.weeksi))%*%solve(cov.d)%*%(as.vector(typica)-mean.weeksi)
typicality.weeksi <- pchisq(d2.weeksi, df = 14, lower.tail = FALSE)
d2.X2 <- (t(as.vector(typica)-mean.X2))%*%solve(cov.d)%*%(as.vector(typica)-mean.X2)
typicality.X2 <- pchisq(d2.X2, df = 14, lower.tail = FALSE)
d2.X3 <- (t(as.vector(typica)-mean.X3))%*%solve(cov.d)%*%(as.vector(typica)-mean.X3)
typicality.X3 <- pchisq(d2.X3, df = 14, lower.tail = FALSE)
d2.X4 <- (t(as.vector(typica)-mean.X4))%*%solve(cov.d)%*%(as.vector(typica)-mean.X4)
typicality.X4 <- pchisq(d2.X4, df = 14, lower.tail = FALSE)
d2.X5 <- (t(as.vector(typica)-mean.X5))%*%solve(cov.d)%*%(as.vector(typica)-mean.X5)
typicality.X5 <- pchisq(d2.X5, df = 14, lower.tail = FALSE)
d2.X8 <- (t(as.vector(typica)-mean.X8))%*%solve(cov.d)%*%(as.vector(typica)-mean.X8)
typicality.X8 <- pchisq(d2.X8, df = 14, lower.tail = FALSE)
d2.X9 <- (t(as.vector(typica)-mean.X9))%*%solve(cov.d)%*%(as.vector(typica)-mean.X9)
typicality.X9 <- pchisq(d2.X9, df = 14, lower.tail = FALSE)
typica.typicality <- as.matrix(c(typicality.A, typicality.B, typicality.E, typicality.H1, typicality.H2, typicality.I, typicality.K, typicality.N, typicality.O, typicality.P, typicality.Q3, typicality.Q4, typicality.Q5, typicality.R, typicality.S, typicality.weeksi, typicality.X2, typicality.X3, typicality.X4, typicality.X5, typicality.X8, typicality.X9))
rownames(typica.typicality) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(typica.typicality) <- "typica.typicality"
typica.typicality <- round(typica.typicality, digits=6)
typica.typicality## typica.typicality
## A 0.000011
## B 0.000002
## E 0.000000
## H1 0.000000
## H2 0.000000
## I 0.000017
## K 0.009161
## N 0.000000
## O 0.000000
## P 0.000846
## Q3 0.011397
## Q4 0.004910
## Q5 0.010976
## R 0.024276
## S 0.000061
## weeksi 0.000792
## X2 0.000000
## X3 0.000000
## X4 0.000000
## X5 0.000207
## X8 0.000024
## X9 0.000093Specimen J54046a (Ozestheria typica) can be assigned to K (34.6%), Q5 (31.9%), Q3 (18.5%), or R (11.3%). However, the typicality score for K is low, while R, Q3, and Q5 receive the highest typicality scores, rendering it more likely that specimen J54046a belongs to one of these lineages.
4.6 typica_mean
### 9 Classify specimen to species
typica_mean <- colMeans(shape_8[c(492:496), c(2:15)]) #new specimen to classify
#typica_mean <- as.matrix(typica_mean)
# get classification score
p.A <- typica_mean%*%cj.A+cj0.A+log(All_singletons.lda$prior[1])
p.B <- typica_mean%*%cj.B+cj0.B+log(All_singletons.lda$prior[2])
p.E <- typica_mean%*%cj.E+cj0.E+log(All_singletons.lda$prior[3])
p.H1 <- typica_mean%*%cj.H1+cj0.H1+log(All_singletons.lda$prior[4])
p.H2 <- typica_mean%*%cj.H2+cj0.H2+log(All_singletons.lda$prior[5])
p.I <- typica_mean%*%cj.I+cj0.I+log(All_singletons.lda$prior[6])
p.K <- typica_mean%*%cj.K+cj0.K+log(All_singletons.lda$prior[7])
p.N <- typica_mean%*%cj.N+cj0.N+log(All_singletons.lda$prior[8])
p.O <- typica_mean%*%cj.O+cj0.O+log(All_singletons.lda$prior[9])
p.P <- typica_mean%*%cj.P+cj0.P+log(All_singletons.lda$prior[10])
p.Q3 <- typica_mean%*%cj.Q3+cj0.Q3+log(All_singletons.lda$prior[11])
p.Q4 <- typica_mean%*%cj.Q4+cj0.Q4+log(All_singletons.lda$prior[12])
p.Q5 <- typica_mean%*%cj.Q5+cj0.Q5+log(All_singletons.lda$prior[13])
p.R <- typica_mean%*%cj.R+cj0.R+log(All_singletons.lda$prior[14])
p.S <- typica_mean%*%cj.S+cj0.S+log(All_singletons.lda$prior[15])
p.weeksi <- typica_mean%*%cj.weeksi+cj0.weeksi+log(All_singletons.lda$prior[16])
p.X2 <- typica_mean%*%cj.X2+cj0.X2+log(All_singletons.lda$prior[17])
p.X3 <- typica_mean%*%cj.X3+cj0.X3+log(All_singletons.lda$prior[18])
p.X4 <- typica_mean%*%cj.X4+cj0.X4+log(All_singletons.lda$prior[19])
p.X5 <- typica_mean%*%cj.X5+cj0.X5+log(All_singletons.lda$prior[20])
p.X8 <- typica_mean%*%cj.X8+cj0.X8+log(All_singletons.lda$prior[21])
p.X9 <- typica_mean%*%cj.X9+cj0.X9+log(All_singletons.lda$prior[22])
# posterior probability
post.A <- (exp(p.A-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.B <- (exp(p.B-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.E <- (exp(p.E-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H1 <- (exp(p.H1-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H2 <- (exp(p.H2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.I <- (exp(p.I-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.K <- (exp(p.K-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.N <- (exp(p.N-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.O <- (exp(p.O-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.P <- (exp(p.P-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q3 <- (exp(p.Q3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q4 <- (exp(p.Q4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q5 <- (exp(p.Q5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.R <- (exp(p.R-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.S <- (exp(p.S-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.weeksi <- (exp(p.weeksi-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X2 <- (exp(p.X2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X3 <- (exp(p.X3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X4 <- (exp(p.X4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X5 <- (exp(p.X5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X8 <- (exp(p.X8-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X9 <- (exp(p.X9-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
typica_mean.posteriors <- as.matrix(c(post.A, post.B, post.E, post.H1, post.H2, post.I, post.K, post.N, post.O, post.P, post.Q3, post.Q4, post.Q5, post.R, post.S, post.weeksi, post.X2, post.X3, post.X4, post.X5, post.X8, post.X9))
rownames(typica_mean.posteriors) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(typica_mean.posteriors) <- "typica_mean.posteriors"
typica_mean.posteriors <- round(typica_mean.posteriors, digits=3)
typica_mean.posteriors## typica_mean.posteriors
## A 0.001
## B 0.000
## E 0.000
## H1 0.000
## H2 0.000
## I 0.000
## K 0.057
## N 0.000
## O 0.000
## P 0.008
## Q3 0.252
## Q4 0.201
## Q5 0.445
## R 0.026
## S 0.001
## weeksi 0.009
## X2 0.000
## X3 0.000
## X4 0.000
## X5 0.000
## X8 0.000
## X9 0.000#typicality probability
d2.A <- (t(as.vector(typica_mean)-mean.A))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.A)
typicality.A <- pchisq(d2.A, df = 14, lower.tail = FALSE)
d2.B <- (t(as.vector(typica_mean)-mean.B))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.B)
typicality.B <- pchisq(d2.B, df = 14, lower.tail = FALSE)
d2.E <- (t(as.vector(typica_mean)-mean.E))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.E)
typicality.E <- pchisq(d2.E, df = 14, lower.tail = FALSE)
d2.H1 <- (t(as.vector(typica_mean)-mean.H1))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.H1)
typicality.H1 <- pchisq(d2.H1, df = 14, lower.tail = FALSE)
d2.H2 <- (t(as.vector(typica_mean)-mean.H2))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.H2)
typicality.H2 <- pchisq(d2.H2, df = 14, lower.tail = FALSE)
d2.I <- (t(as.vector(typica_mean)-mean.I))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.I)
typicality.I <- pchisq(d2.I, df = 14, lower.tail = FALSE)
d2.K <- (t(as.vector(typica_mean)-mean.K))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.K)
typicality.K <- pchisq(d2.K, df = 14, lower.tail = FALSE)
d2.N <- (t(as.vector(typica_mean)-mean.N))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.N)
typicality.N <- pchisq(d2.N, df = 14, lower.tail = FALSE)
d2.O <- (t(as.vector(typica_mean)-mean.O))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.O)
typicality.O <- pchisq(d2.O, df = 14, lower.tail = FALSE)
d2.P <- (t(as.vector(typica_mean)-mean.P))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.P)
typicality.P <- pchisq(d2.P, df = 14, lower.tail = FALSE)
d2.Q3 <- (t(as.vector(typica_mean)-mean.Q3))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.Q3)
typicality.Q3 <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q4 <- (t(as.vector(typica_mean)-mean.Q4))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.Q4)
typicality.Q4 <- pchisq(d2.Q4, df = 14, lower.tail = FALSE)
d2.Q5 <- (t(as.vector(typica_mean)-mean.Q5))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.Q5)
typicality.Q5 <- pchisq(d2.Q5, df = 14, lower.tail = FALSE)
d2.R <- (t(as.vector(typica_mean)-mean.R))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.R)
typicality.R <- pchisq(d2.R, df = 14, lower.tail = FALSE)
d2.S <- (t(as.vector(typica_mean)-mean.S))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.S)
typicality.S <- pchisq(d2.S, df = 14, lower.tail = FALSE)
d2.weeksi <- (t(as.vector(typica_mean)-mean.weeksi))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.weeksi)
typicality.weeksi <- pchisq(d2.weeksi, df = 14, lower.tail = FALSE)
d2.X2 <- (t(as.vector(typica_mean)-mean.X2))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.X2)
typicality.X2 <- pchisq(d2.X2, df = 14, lower.tail = FALSE)
d2.X3 <- (t(as.vector(typica_mean)-mean.X3))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.X3)
typicality.X3 <- pchisq(d2.X3, df = 14, lower.tail = FALSE)
d2.X4 <- (t(as.vector(typica_mean)-mean.X4))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.X4)
typicality.X4 <- pchisq(d2.X4, df = 14, lower.tail = FALSE)
d2.X5 <- (t(as.vector(typica_mean)-mean.X5))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.X5)
typicality.X5 <- pchisq(d2.X5, df = 14, lower.tail = FALSE)
d2.X8 <- (t(as.vector(typica_mean)-mean.X8))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.X8)
typicality.X8 <- pchisq(d2.X8, df = 14, lower.tail = FALSE)
d2.X9 <- (t(as.vector(typica_mean)-mean.X9))%*%solve(cov.d)%*%(as.vector(typica_mean)-mean.X9)
typicality.X9 <- pchisq(d2.X9, df = 14, lower.tail = FALSE)
typica_mean.typicality <- as.matrix(c(typicality.A, typicality.B, typicality.E, typicality.H1, typicality.H2, typicality.I, typicality.K, typicality.N, typicality.O, typicality.P, typicality.Q3, typicality.Q4, typicality.Q5, typicality.R, typicality.S, typicality.weeksi, typicality.X2, typicality.X3, typicality.X4, typicality.X5, typicality.X8, typicality.X9))
rownames(typica_mean.typicality) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(typica_mean.typicality) <- "typica_mean.typicality"
typica_mean.typicality <- round(typica_mean.typicality, digits=6)
typica_mean.typicality## typica_mean.typicality
## A 0.013473
## B 0.005274
## E 0.013856
## H1 0.000001
## H2 0.000000
## I 0.003927
## K 0.085106
## N 0.000004
## O 0.000007
## P 0.057969
## Q3 0.268161
## Q4 0.299358
## Q5 0.264596
## R 0.213662
## S 0.006684
## weeksi 0.049603
## X2 0.000000
## X3 0.000091
## X4 0.000000
## X5 0.005128
## X8 0.001009
## X9 0.001816The mean shape of the five Ozestheria typica syntypes can be assigned to lineages Q5 (44.5%), Q3 (25.2%), Q4 (20.1%), and K (5.7%). It is thus very likely that one of the closely related Q lineages can be assigned to O. typica. Q4, Q3, and Q5 also receive the highest typicality scores.
4.7 mariae C34420A
### 9 Classify specimen to species
mariae <- shape_8[c(399), c(2:15)] #new specimen to classify
mariae <- as.matrix(mariae)
# get classification score
p.A <- mariae%*%cj.A+cj0.A+log(All_singletons.lda$prior[1])
p.B <- mariae%*%cj.B+cj0.B+log(All_singletons.lda$prior[2])
p.E <- mariae%*%cj.E+cj0.E+log(All_singletons.lda$prior[3])
p.H1 <- mariae%*%cj.H1+cj0.H1+log(All_singletons.lda$prior[4])
p.H2 <- mariae%*%cj.H2+cj0.H2+log(All_singletons.lda$prior[5])
p.I <- mariae%*%cj.I+cj0.I+log(All_singletons.lda$prior[6])
p.K <- mariae%*%cj.K+cj0.K+log(All_singletons.lda$prior[7])
p.N <- mariae%*%cj.N+cj0.N+log(All_singletons.lda$prior[8])
p.O <- mariae%*%cj.O+cj0.O+log(All_singletons.lda$prior[9])
p.P <- mariae%*%cj.P+cj0.P+log(All_singletons.lda$prior[10])
p.Q3 <- mariae%*%cj.Q3+cj0.Q3+log(All_singletons.lda$prior[11])
p.Q4 <- mariae%*%cj.Q4+cj0.Q4+log(All_singletons.lda$prior[12])
p.Q5 <- mariae%*%cj.Q5+cj0.Q5+log(All_singletons.lda$prior[13])
p.R <- mariae%*%cj.R+cj0.R+log(All_singletons.lda$prior[14])
p.S <- mariae%*%cj.S+cj0.S+log(All_singletons.lda$prior[15])
p.weeksi <- mariae%*%cj.weeksi+cj0.weeksi+log(All_singletons.lda$prior[16])
p.X2 <- mariae%*%cj.X2+cj0.X2+log(All_singletons.lda$prior[17])
p.X3 <- mariae%*%cj.X3+cj0.X3+log(All_singletons.lda$prior[18])
p.X4 <- mariae%*%cj.X4+cj0.X4+log(All_singletons.lda$prior[19])
p.X5 <- mariae%*%cj.X5+cj0.X5+log(All_singletons.lda$prior[20])
p.X8 <- mariae%*%cj.X8+cj0.X8+log(All_singletons.lda$prior[21])
p.X9 <- mariae%*%cj.X9+cj0.X9+log(All_singletons.lda$prior[22])
# posterior probability
post.A <- (exp(p.A-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.B <- (exp(p.B-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.E <- (exp(p.E-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H1 <- (exp(p.H1-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H2 <- (exp(p.H2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.I <- (exp(p.I-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.K <- (exp(p.K-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.N <- (exp(p.N-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.O <- (exp(p.O-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.P <- (exp(p.P-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q3 <- (exp(p.Q3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q4 <- (exp(p.Q4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q5 <- (exp(p.Q5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.R <- (exp(p.R-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.S <- (exp(p.S-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.weeksi <- (exp(p.weeksi-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X2 <- (exp(p.X2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X3 <- (exp(p.X3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X4 <- (exp(p.X4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X5 <- (exp(p.X5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X8 <- (exp(p.X8-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X9 <- (exp(p.X9-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
mariae.posteriors <- as.matrix(c(post.A, post.B, post.E, post.H1, post.H2, post.I, post.K, post.N, post.O, post.P, post.Q3, post.Q4, post.Q5, post.R, post.S, post.weeksi, post.X2, post.X3, post.X4, post.X5, post.X8, post.X9))
rownames(mariae.posteriors) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(mariae.posteriors) <- "mariae.posteriors"
mariae.posteriors <- round(mariae.posteriors, digits=3)
mariae.posteriors## mariae.posteriors
## A 0.000
## B 0.000
## E 0.000
## H1 0.004
## H2 0.001
## I 0.001
## K 0.125
## N 0.798
## O 0.001
## P 0.004
## Q3 0.000
## Q4 0.000
## Q5 0.000
## R 0.000
## S 0.013
## weeksi 0.000
## X2 0.000
## X3 0.000
## X4 0.000
## X5 0.020
## X8 0.000
## X9 0.034#typicality probability
d2.A <- (t(as.vector(mariae)-mean.A))%*%solve(cov.d)%*%(as.vector(mariae)-mean.A)
typicality.A <- pchisq(d2.A, df = 14, lower.tail = FALSE)
d2.B <- (t(as.vector(mariae)-mean.B))%*%solve(cov.d)%*%(as.vector(mariae)-mean.B)
typicality.B <- pchisq(d2.B, df = 14, lower.tail = FALSE)
d2.E <- (t(as.vector(mariae)-mean.E))%*%solve(cov.d)%*%(as.vector(mariae)-mean.E)
typicality.E <- pchisq(d2.E, df = 14, lower.tail = FALSE)
d2.H1 <- (t(as.vector(mariae)-mean.H1))%*%solve(cov.d)%*%(as.vector(mariae)-mean.H1)
typicality.H1 <- pchisq(d2.H1, df = 14, lower.tail = FALSE)
d2.H2 <- (t(as.vector(mariae)-mean.H2))%*%solve(cov.d)%*%(as.vector(mariae)-mean.H2)
typicality.H2 <- pchisq(d2.H2, df = 14, lower.tail = FALSE)
d2.I <- (t(as.vector(mariae)-mean.I))%*%solve(cov.d)%*%(as.vector(mariae)-mean.I)
typicality.I <- pchisq(d2.I, df = 14, lower.tail = FALSE)
d2.K <- (t(as.vector(mariae)-mean.K))%*%solve(cov.d)%*%(as.vector(mariae)-mean.K)
typicality.K <- pchisq(d2.K, df = 14, lower.tail = FALSE)
d2.N <- (t(as.vector(mariae)-mean.N))%*%solve(cov.d)%*%(as.vector(mariae)-mean.N)
typicality.N <- pchisq(d2.N, df = 14, lower.tail = FALSE)
d2.O <- (t(as.vector(mariae)-mean.O))%*%solve(cov.d)%*%(as.vector(mariae)-mean.O)
typicality.O <- pchisq(d2.O, df = 14, lower.tail = FALSE)
d2.P <- (t(as.vector(mariae)-mean.P))%*%solve(cov.d)%*%(as.vector(mariae)-mean.P)
typicality.P <- pchisq(d2.P, df = 14, lower.tail = FALSE)
d2.Q3 <- (t(as.vector(mariae)-mean.Q3))%*%solve(cov.d)%*%(as.vector(mariae)-mean.Q3)
typicality.Q3 <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q4 <- (t(as.vector(mariae)-mean.Q4))%*%solve(cov.d)%*%(as.vector(mariae)-mean.Q4)
typicality.Q4 <- pchisq(d2.Q4, df = 14, lower.tail = FALSE)
d2.Q5 <- (t(as.vector(mariae)-mean.Q5))%*%solve(cov.d)%*%(as.vector(mariae)-mean.Q5)
typicality.Q5 <- pchisq(d2.Q5, df = 14, lower.tail = FALSE)
d2.R <- (t(as.vector(mariae)-mean.R))%*%solve(cov.d)%*%(as.vector(mariae)-mean.R)
typicality.R <- pchisq(d2.R, df = 14, lower.tail = FALSE)
d2.S <- (t(as.vector(mariae)-mean.S))%*%solve(cov.d)%*%(as.vector(mariae)-mean.S)
typicality.S <- pchisq(d2.S, df = 14, lower.tail = FALSE)
d2.weeksi <- (t(as.vector(mariae)-mean.weeksi))%*%solve(cov.d)%*%(as.vector(mariae)-mean.weeksi)
typicality.weeksi <- pchisq(d2.weeksi, df = 14, lower.tail = FALSE)
d2.X2 <- (t(as.vector(mariae)-mean.X2))%*%solve(cov.d)%*%(as.vector(mariae)-mean.X2)
typicality.X2 <- pchisq(d2.X2, df = 14, lower.tail = FALSE)
d2.X3 <- (t(as.vector(mariae)-mean.X3))%*%solve(cov.d)%*%(as.vector(mariae)-mean.X3)
typicality.X3 <- pchisq(d2.X3, df = 14, lower.tail = FALSE)
d2.X4 <- (t(as.vector(mariae)-mean.X4))%*%solve(cov.d)%*%(as.vector(mariae)-mean.X4)
typicality.X4 <- pchisq(d2.X4, df = 14, lower.tail = FALSE)
d2.X5 <- (t(as.vector(mariae)-mean.X5))%*%solve(cov.d)%*%(as.vector(mariae)-mean.X5)
typicality.X5 <- pchisq(d2.X5, df = 14, lower.tail = FALSE)
d2.X8 <- (t(as.vector(mariae)-mean.X8))%*%solve(cov.d)%*%(as.vector(mariae)-mean.X8)
typicality.X8 <- pchisq(d2.X8, df = 14, lower.tail = FALSE)
d2.X9 <- (t(as.vector(mariae)-mean.X9))%*%solve(cov.d)%*%(as.vector(mariae)-mean.X9)
typicality.X9 <- pchisq(d2.X9, df = 14, lower.tail = FALSE)
mariae.typicality <- as.matrix(c(typicality.A, typicality.B, typicality.E, typicality.H1, typicality.H2, typicality.I, typicality.K, typicality.N, typicality.O, typicality.P, typicality.Q3, typicality.Q4, typicality.Q5, typicality.R, typicality.S, typicality.weeksi, typicality.X2, typicality.X3, typicality.X4, typicality.X5, typicality.X8, typicality.X9))
rownames(mariae.typicality) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(mariae.typicality) <- "mariae.typicality"
mariae.typicality <- round(mariae.typicality, digits=6)
mariae.typicality## mariae.typicality
## A 0.000001
## B 0.000000
## E 0.000001
## H1 0.072018
## H2 0.057468
## I 0.056906
## K 0.222338
## N 0.486089
## O 0.021456
## P 0.079527
## Q3 0.001090
## Q4 0.000000
## Q5 0.000000
## R 0.000037
## S 0.071952
## weeksi 0.003370
## X2 0.000000
## X3 0.000000
## X4 0.000000
## X5 0.323350
## X8 0.006557
## X9 0.301398Specimen C34420A (Ozestheria mariae) can be assigned to N (79.8%) or K (12.5%). The shape of O. mariae represents a common shape within the Ozestheria morphospace.
4.8 mariae_mean
### 9 Classify specimen to species
mariae_mean <- colMeans(shape_8[c(399:403), c(2:15)]) #new specimen to classify
# get classification score
p.A <- mariae_mean%*%cj.A+cj0.A+log(All_singletons.lda$prior[1])
p.B <- mariae_mean%*%cj.B+cj0.B+log(All_singletons.lda$prior[2])
p.E <- mariae_mean%*%cj.E+cj0.E+log(All_singletons.lda$prior[3])
p.H1 <- mariae_mean%*%cj.H1+cj0.H1+log(All_singletons.lda$prior[4])
p.H2 <- mariae_mean%*%cj.H2+cj0.H2+log(All_singletons.lda$prior[5])
p.I <- mariae_mean%*%cj.I+cj0.I+log(All_singletons.lda$prior[6])
p.K <- mariae_mean%*%cj.K+cj0.K+log(All_singletons.lda$prior[7])
p.N <- mariae_mean%*%cj.N+cj0.N+log(All_singletons.lda$prior[8])
p.O <- mariae_mean%*%cj.O+cj0.O+log(All_singletons.lda$prior[9])
p.P <- mariae_mean%*%cj.P+cj0.P+log(All_singletons.lda$prior[10])
p.Q3 <- mariae_mean%*%cj.Q3+cj0.Q3+log(All_singletons.lda$prior[11])
p.Q4 <- mariae_mean%*%cj.Q4+cj0.Q4+log(All_singletons.lda$prior[12])
p.Q5 <- mariae_mean%*%cj.Q5+cj0.Q5+log(All_singletons.lda$prior[13])
p.R <- mariae_mean%*%cj.R+cj0.R+log(All_singletons.lda$prior[14])
p.S <- mariae_mean%*%cj.S+cj0.S+log(All_singletons.lda$prior[15])
p.weeksi <- mariae_mean%*%cj.weeksi+cj0.weeksi+log(All_singletons.lda$prior[16])
p.X2 <- mariae_mean%*%cj.X2+cj0.X2+log(All_singletons.lda$prior[17])
p.X3 <- mariae_mean%*%cj.X3+cj0.X3+log(All_singletons.lda$prior[18])
p.X4 <- mariae_mean%*%cj.X4+cj0.X4+log(All_singletons.lda$prior[19])
p.X5 <- mariae_mean%*%cj.X5+cj0.X5+log(All_singletons.lda$prior[20])
p.X8 <- mariae_mean%*%cj.X8+cj0.X8+log(All_singletons.lda$prior[21])
p.X9 <- mariae_mean%*%cj.X9+cj0.X9+log(All_singletons.lda$prior[22])
# posterior probability
post.A <- (exp(p.A-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.B <- (exp(p.B-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.E <- (exp(p.E-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H1 <- (exp(p.H1-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.H2 <- (exp(p.H2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.I <- (exp(p.I-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.K <- (exp(p.K-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.N <- (exp(p.N-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.O <- (exp(p.O-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.P <- (exp(p.P-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q3 <- (exp(p.Q3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q4 <- (exp(p.Q4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.Q5 <- (exp(p.Q5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.R <- (exp(p.R-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.S <- (exp(p.S-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.weeksi <- (exp(p.weeksi-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X2 <- (exp(p.X2-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X3 <- (exp(p.X3-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X4 <- (exp(p.X4-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X5 <- (exp(p.X5-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X8 <- (exp(p.X8-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
post.X9 <- (exp(p.X9-p.A))/(exp(p.B-p.A)+exp(p.A-p.A)+exp(p.E-p.A)+exp(p.H1-p.A)+exp(p.H2-p.A)+exp(p.I-p.A)+exp(p.K-p.A)+exp(p.N-p.A)+exp(p.O-p.A)+exp(p.P-p.A)+exp(p.Q3-p.A)+exp(p.Q4-p.A)+exp(p.Q5-p.A)+exp(p.R-p.A)+exp(p.S-p.A)+exp(p.weeksi-p.A)+exp(p.X2-p.A)+exp(p.X3-p.A)+exp(p.X4-p.A)+exp(p.X5-p.A)+exp(p.X8-p.A)+exp(p.X9-p.A))
mariae_mean.posteriors <- as.matrix(c(post.A, post.B, post.E, post.H1, post.H2, post.I, post.K, post.N, post.O, post.P, post.Q3, post.Q4, post.Q5, post.R, post.S, post.weeksi, post.X2, post.X3, post.X4, post.X5, post.X8, post.X9))
rownames(mariae_mean.posteriors) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(mariae_mean.posteriors) <- "mariae_mean.posteriors"
mariae_mean.posteriors <- round(mariae_mean.posteriors, digits=3)
mariae_mean.posteriors## mariae_mean.posteriors
## A 0.000
## B 0.000
## E 0.000
## H1 0.001
## H2 0.000
## I 0.002
## K 0.543
## N 0.378
## O 0.000
## P 0.008
## Q3 0.000
## Q4 0.000
## Q5 0.000
## R 0.000
## S 0.007
## weeksi 0.000
## X2 0.000
## X3 0.000
## X4 0.000
## X5 0.052
## X8 0.000
## X9 0.009#typicality probability
d2.A <- (t(as.vector(mariae_mean)-mean.A))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.A)
typicality.A <- pchisq(d2.A, df = 14, lower.tail = FALSE)
d2.B <- (t(as.vector(mariae_mean)-mean.B))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.B)
typicality.B <- pchisq(d2.B, df = 14, lower.tail = FALSE)
d2.E <- (t(as.vector(mariae_mean)-mean.E))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.E)
typicality.E <- pchisq(d2.E, df = 14, lower.tail = FALSE)
d2.H1 <- (t(as.vector(mariae_mean)-mean.H1))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.H1)
typicality.H1 <- pchisq(d2.H1, df = 14, lower.tail = FALSE)
d2.H2 <- (t(as.vector(mariae_mean)-mean.H2))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.H2)
typicality.H2 <- pchisq(d2.H2, df = 14, lower.tail = FALSE)
d2.I <- (t(as.vector(mariae_mean)-mean.I))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.I)
typicality.I <- pchisq(d2.I, df = 14, lower.tail = FALSE)
d2.K <- (t(as.vector(mariae_mean)-mean.K))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.K)
typicality.K <- pchisq(d2.K, df = 14, lower.tail = FALSE)
d2.N <- (t(as.vector(mariae_mean)-mean.N))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.N)
typicality.N <- pchisq(d2.N, df = 14, lower.tail = FALSE)
d2.O <- (t(as.vector(mariae_mean)-mean.O))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.O)
typicality.O <- pchisq(d2.O, df = 14, lower.tail = FALSE)
d2.P <- (t(as.vector(mariae_mean)-mean.P))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.P)
typicality.P <- pchisq(d2.P, df = 14, lower.tail = FALSE)
d2.Q3 <- (t(as.vector(mariae_mean)-mean.Q3))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.Q3)
typicality.Q3 <- pchisq(d2.Q3, df = 14, lower.tail = FALSE)
d2.Q4 <- (t(as.vector(mariae_mean)-mean.Q4))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.Q4)
typicality.Q4 <- pchisq(d2.Q4, df = 14, lower.tail = FALSE)
d2.Q5 <- (t(as.vector(mariae_mean)-mean.Q5))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.Q5)
typicality.Q5 <- pchisq(d2.Q5, df = 14, lower.tail = FALSE)
d2.R <- (t(as.vector(mariae_mean)-mean.R))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.R)
typicality.R <- pchisq(d2.R, df = 14, lower.tail = FALSE)
d2.S <- (t(as.vector(mariae_mean)-mean.S))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.S)
typicality.S <- pchisq(d2.S, df = 14, lower.tail = FALSE)
d2.weeksi <- (t(as.vector(mariae_mean)-mean.weeksi))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.weeksi)
typicality.weeksi <- pchisq(d2.weeksi, df = 14, lower.tail = FALSE)
d2.X2 <- (t(as.vector(mariae_mean)-mean.X2))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.X2)
typicality.X2 <- pchisq(d2.X2, df = 14, lower.tail = FALSE)
d2.X3 <- (t(as.vector(mariae_mean)-mean.X3))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.X3)
typicality.X3 <- pchisq(d2.X3, df = 14, lower.tail = FALSE)
d2.X4 <- (t(as.vector(mariae_mean)-mean.X4))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.X4)
typicality.X4 <- pchisq(d2.X4, df = 14, lower.tail = FALSE)
d2.X5 <- (t(as.vector(mariae_mean)-mean.X5))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.X5)
typicality.X5 <- pchisq(d2.X5, df = 14, lower.tail = FALSE)
d2.X8 <- (t(as.vector(mariae_mean)-mean.X8))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.X8)
typicality.X8 <- pchisq(d2.X8, df = 14, lower.tail = FALSE)
d2.X9 <- (t(as.vector(mariae_mean)-mean.X9))%*%solve(cov.d)%*%(as.vector(mariae_mean)-mean.X9)
typicality.X9 <- pchisq(d2.X9, df = 14, lower.tail = FALSE)
mariae_mean.typicality <- as.matrix(c(typicality.A, typicality.B, typicality.E, typicality.H1, typicality.H2, typicality.I, typicality.K, typicality.N, typicality.O, typicality.P, typicality.Q3, typicality.Q4, typicality.Q5, typicality.R, typicality.S, typicality.weeksi, typicality.X2, typicality.X3, typicality.X4, typicality.X5, typicality.X8, typicality.X9))
rownames(mariae_mean.typicality) <- c("A", "B", "E", "H1", "H2", "I", "K", "N", "O", "P", "Q3", "Q4", "Q5", "R", "S", "weeksi", "X2", "X3", "X4", "X5", "X8", "X9")
colnames(mariae_mean.typicality) <- "mariae_mean.typicality"
mariae_mean.typicality <- round(mariae_mean.typicality, digits=6)
mariae_mean.typicality## mariae_mean.typicality
## A 0.000001
## B 0.000001
## E 0.000835
## H1 0.112863
## H2 0.027490
## I 0.324782
## K 0.793840
## N 0.773397
## O 0.055342
## P 0.342327
## Q3 0.004249
## Q4 0.000000
## Q5 0.000002
## R 0.003172
## S 0.187329
## weeksi 0.039361
## X2 0.000000
## X3 0.000000
## X4 0.000000
## X5 0.844772
## X8 0.041559
## X9 0.476822The mean shape of Ozestheria mariae can be assigned to K (54.3%), N (37.8%) or X5 (5.2%). X5 receives the highest typicality score. The shape of O. mariae represents a common shape within the Ozestheria morphospace.