SERA POOLS Study
Model 2 Results
R. Noah Padgett
2022-02-01
Last updated: 2022-02-02
Checks: 4 2
Knit directory: Padgett-Dissertation/
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# Load packages & utility functions
source("code/load_packages.R")
source("code/load_utility_functions.R")
# environment options
options(scipen = 999, digits=3)POOLS Data
library(readxl)
mydata <- read_excel("data/pools/POOLS_data_2020-11-16.xlsx")
use.var <- c(paste0("Q4_",c(3:5,9,11,15,18)),
paste0("Q5_",c(1:3,5:6,12)),
paste0("Q6_",c(2,5:8, 11)),
paste0("Q7_",c(2, 4:5, 7:8, 14)))
# trichotomize
f <- function(x){
y=numeric(length(x))
for(i in 1:length(x)){
if(x[i] < 3){
y[i] = 1
}
if(x[i] == 3){
y[i] = 2
}
if(x[i] > 3){
y[i] = 3
}
}
return(y)
}
mydata <- na.omit(mydata[, use.var])
mydata <- apply(mydata, 2, f) %>%
as.data.frame()
psych::describe(
mydata
) vars n mean sd median trimmed mad min max range skew kurtosis se
Q4_3 1 490 1.62 0.65 2 1.53 1.48 1 3 2 0.57 -0.68 0.03
Q4_4 2 490 1.64 0.65 2 1.56 1.48 1 3 2 0.51 -0.71 0.03
Q4_5 3 490 1.52 0.68 1 1.40 0.00 1 3 2 0.92 -0.36 0.03
Q4_9 4 490 1.65 0.76 1 1.56 0.00 1 3 2 0.69 -0.96 0.03
Q4_11 5 490 1.64 0.72 1 1.55 0.00 1 3 2 0.66 -0.85 0.03
Q4_15 6 490 1.58 0.68 1 1.47 0.00 1 3 2 0.74 -0.59 0.03
Q4_18 7 490 1.52 0.63 1 1.43 0.00 1 3 2 0.81 -0.38 0.03
Q5_1 8 490 1.73 0.77 2 1.66 1.48 1 3 2 0.50 -1.16 0.03
Q5_2 9 490 2.00 0.86 2 2.00 1.48 1 3 2 0.00 -1.64 0.04
Q5_3 10 490 1.79 0.81 2 1.73 1.48 1 3 2 0.41 -1.37 0.04
Q5_5 11 490 2.33 0.81 3 2.41 0.00 1 3 2 -0.67 -1.18 0.04
Q5_6 12 490 1.94 0.77 2 1.93 1.48 1 3 2 0.09 -1.33 0.03
Q5_12 13 490 1.92 0.78 2 1.90 1.48 1 3 2 0.14 -1.36 0.04
Q6_2 14 490 1.40 0.67 1 1.24 0.00 1 3 2 1.42 0.64 0.03
Q6_5 15 490 1.66 0.80 1 1.58 0.00 1 3 2 0.68 -1.11 0.04
Q6_6 16 490 1.22 0.52 1 1.09 0.00 1 3 2 2.29 4.28 0.02
Q6_7 17 490 1.45 0.66 1 1.32 0.00 1 3 2 1.17 0.14 0.03
Q6_8 18 490 1.43 0.65 1 1.31 0.00 1 3 2 1.21 0.27 0.03
Q6_11 19 490 1.85 0.76 2 1.81 1.48 1 3 2 0.26 -1.22 0.03
Q7_2 20 490 1.74 0.69 2 1.67 1.48 1 3 2 0.39 -0.89 0.03
Q7_4 21 490 1.89 0.79 2 1.86 1.48 1 3 2 0.20 -1.37 0.04
Q7_5 22 490 1.89 0.76 2 1.86 1.48 1 3 2 0.19 -1.24 0.03
Q7_7 23 490 2.43 0.78 3 2.54 0.00 1 3 2 -0.91 -0.76 0.04
Q7_8 24 490 1.87 0.75 2 1.84 1.48 1 3 2 0.21 -1.21 0.03
Q7_14 25 490 2.39 0.76 3 2.49 0.00 1 3 2 -0.78 -0.85 0.03DWLS
mod <- '
EL =~ 1*Q4_3 + lam44*Q4_4 + lam45*Q4_5 + lam49*Q4_9 + lam411*Q4_11 + lam415*Q4_15 + lam418*Q4_18
SC =~ 1*Q5_1 + lam52*Q5_2 + lam53*Q5_3 + lam55*Q5_5 + lam56*Q5_6 + lam512*Q5_12
IN =~ 1*Q6_2 + lam65*Q6_5 + lam66*Q6_6 + lam67*Q6_7 + lam68*Q6_8 + lam611*Q6_11
EN =~ 1*Q7_2 + lam74*Q7_4 + lam75*Q7_5 + lam77*Q7_7 + lam78*Q7_8 + lam714*Q7_14
# Factor covarainces
EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN
# Factor Reliabilities
rEL := ((1 + lam44 + lam45 + lam49 + lam411 + lam415 + lam418)**2)/((1 + lam44 + lam45 + lam49 + lam411 + lam415 + lam418)**2 + 7)
rSC := ((1 + lam52 + lam53 + lam55 + lam56 + lam512)**2)/((1 + lam52 + lam53 + lam55 + lam56 + lam512)**2 + 6)
rIN := ((1 + lam65 + lam66 + lam67 + lam68 + lam611)**2)/((1 + lam65 + lam66 + lam67 + lam68 + lam611)**2 + 6)
rEN := ((1 + lam74 + lam75 + lam77 + lam78 + lam714)**2)/((1 + lam74 + lam75 + lam77 + lam78 + lam714)**2 + 6)
'
fit.dwls <- lavaan::cfa(mod, data=mydata, ordered=T, parameterization="theta")
summary(fit.dwls, standardized=T, fit.measures=T)lavaan 0.6-7 ended normally after 66 iterations
Estimator DWLS
Optimization method NLMINB
Number of free parameters 81
Number of observations 490
Model Test User Model:
Standard Robust
Test Statistic 593.869 765.951
Degrees of freedom 269 269
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.883
Shift parameter 93.760
simple second-order correction
Model Test Baseline Model:
Test statistic 32729.962 10489.239
Degrees of freedom 300 300
P-value 0.000 0.000
Scaling correction factor 3.183
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.990 0.951
Tucker-Lewis Index (TLI) 0.989 0.946
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.050 0.061
90 Percent confidence interval - lower 0.044 0.056
90 Percent confidence interval - upper 0.055 0.067
P-value RMSEA <= 0.05 0.529 0.000
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.065 0.065
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL =~
Q4_3 1.000 1.234 0.777
Q4_4 (lm44) 1.445 0.145 9.972 0.000 1.783 0.872
Q4_5 (lm45) 0.949 0.101 9.390 0.000 1.171 0.760
Q4_9 (lm49) 0.763 0.084 9.048 0.000 0.942 0.686
Q4_11 (l411) 1.048 0.110 9.536 0.000 1.293 0.791
Q4_15 (l415) 0.994 0.107 9.309 0.000 1.227 0.775
Q4_18 (l418) 1.272 0.137 9.295 0.000 1.569 0.843
SC =~
Q5_1 1.000 1.082 0.734
Q5_2 (lm52) 0.976 0.119 8.171 0.000 1.056 0.726
Q5_3 (lm53) 0.944 0.124 7.587 0.000 1.021 0.714
Q5_5 (lm55) 0.803 0.114 7.052 0.000 0.869 0.656
Q5_6 (lm56) 1.224 0.162 7.549 0.000 1.324 0.798
Q5_12 (l512) 1.188 0.160 7.446 0.000 1.286 0.789
IN =~
Q6_2 1.000 1.054 0.725
Q6_5 (lm65) 0.618 0.095 6.522 0.000 0.651 0.546
Q6_6 (lm66) 1.704 0.290 5.882 0.000 1.796 0.874
Q6_7 (lm67) 1.518 0.220 6.893 0.000 1.600 0.848
Q6_8 (lm68) 1.234 0.157 7.839 0.000 1.301 0.793
Q6_11 (l611) 1.602 0.256 6.258 0.000 1.688 0.860
EN =~
Q7_2 1.000 1.243 0.779
Q7_4 (lm74) 0.800 0.088 9.095 0.000 0.994 0.705
Q7_5 (lm75) 1.108 0.132 8.392 0.000 1.378 0.809
Q7_7 (lm77) 0.875 0.125 6.996 0.000 1.087 0.736
Q7_8 (lm78) 0.867 0.095 9.155 0.000 1.078 0.733
Q7_14 (l714) 0.672 0.088 7.626 0.000 0.835 0.641
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL ~~
SC 0.915 0.139 6.573 0.000 0.685 0.685
IN 0.973 0.152 6.402 0.000 0.748 0.748
EN 1.193 0.161 7.417 0.000 0.778 0.778
SC ~~
IN 0.740 0.129 5.718 0.000 0.649 0.649
EN 1.080 0.161 6.691 0.000 0.803 0.803
IN ~~
EN 0.979 0.156 6.265 0.000 0.747 0.747
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q4_3 0.000 0.000 0.000
.Q4_4 0.000 0.000 0.000
.Q4_5 0.000 0.000 0.000
.Q4_9 0.000 0.000 0.000
.Q4_11 0.000 0.000 0.000
.Q4_15 0.000 0.000 0.000
.Q4_18 0.000 0.000 0.000
.Q5_1 0.000 0.000 0.000
.Q5_2 0.000 0.000 0.000
.Q5_3 0.000 0.000 0.000
.Q5_5 0.000 0.000 0.000
.Q5_6 0.000 0.000 0.000
.Q5_12 0.000 0.000 0.000
.Q6_2 0.000 0.000 0.000
.Q6_5 0.000 0.000 0.000
.Q6_6 0.000 0.000 0.000
.Q6_7 0.000 0.000 0.000
.Q6_8 0.000 0.000 0.000
.Q6_11 0.000 0.000 0.000
.Q7_2 0.000 0.000 0.000
.Q7_4 0.000 0.000 0.000
.Q7_5 0.000 0.000 0.000
.Q7_7 0.000 0.000 0.000
.Q7_8 0.000 0.000 0.000
.Q7_14 0.000 0.000 0.000
EL 0.000 0.000 0.000
SC 0.000 0.000 0.000
IN 0.000 0.000 0.000
EN 0.000 0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Q4_3|t1 -0.106 0.090 -1.181 0.238 -0.106 -0.067
Q4_3|t2 2.073 0.136 15.218 0.000 2.073 1.305
Q4_4|t1 -0.241 0.115 -2.098 0.036 -0.241 -0.118
Q4_4|t2 2.644 0.185 14.260 0.000 2.644 1.293
Q4_5|t1 0.317 0.090 3.516 0.000 0.317 0.206
Q4_5|t2 1.938 0.128 15.163 0.000 1.938 1.259
Q4_9|t1 0.091 0.078 1.168 0.243 0.091 0.067
Q4_9|t2 1.292 0.097 13.316 0.000 1.292 0.941
Q4_11|t1 0.017 0.093 0.180 0.857 0.017 0.010
Q4_11|t2 1.716 0.126 13.628 0.000 1.716 1.050
Q4_15|t1 0.105 0.091 1.163 0.245 0.105 0.067
Q4_15|t2 1.974 0.139 14.200 0.000 1.974 1.247
Q4_18|t1 0.258 0.109 2.368 0.018 0.258 0.139
Q4_18|t2 2.672 0.190 14.079 0.000 2.672 1.436
Q5_1|t1 -0.121 0.083 -1.451 0.147 -0.121 -0.082
Q5_1|t2 1.251 0.104 12.001 0.000 1.251 0.849
Q5_2|t1 -0.493 0.085 -5.781 0.000 -0.493 -0.339
Q5_2|t2 0.501 0.084 5.987 0.000 0.501 0.344
Q5_3|t1 -0.146 0.081 -1.813 0.070 -0.146 -0.102
Q5_3|t2 0.987 0.094 10.505 0.000 0.987 0.691
Q5_5|t1 -1.021 0.090 -11.329 0.000 -1.021 -0.771
Q5_5|t2 -0.163 0.076 -2.143 0.032 -0.163 -0.123
Q5_6|t1 -0.737 0.101 -7.317 0.000 -0.737 -0.444
Q5_6|t2 1.000 0.106 9.460 0.000 1.000 0.602
Q5_12|t1 -0.641 0.098 -6.572 0.000 -0.641 -0.394
Q5_12|t2 1.001 0.108 9.310 0.000 1.001 0.615
Q6_2|t1 0.788 0.098 8.044 0.000 0.788 0.542
Q6_2|t2 1.845 0.133 13.877 0.000 1.845 1.270
Q6_5|t1 0.128 0.068 1.886 0.059 0.128 0.108
Q6_5|t2 0.979 0.079 12.392 0.000 0.979 0.820
Q6_6|t1 1.934 0.262 7.394 0.000 1.934 0.941
Q6_6|t2 3.402 0.391 8.706 0.000 3.402 1.655
Q6_7|t1 0.701 0.126 5.564 0.000 0.701 0.372
Q6_7|t2 2.509 0.216 11.601 0.000 2.509 1.330
Q6_8|t1 0.655 0.107 6.131 0.000 0.655 0.399
Q6_8|t2 2.244 0.170 13.237 0.000 2.244 1.368
Q6_11|t1 -0.633 0.120 -5.279 0.000 -0.633 -0.323
Q6_11|t2 1.513 0.156 9.676 0.000 1.513 0.771
Q7_2|t1 -0.396 0.092 -4.325 0.000 -0.396 -0.248
Q7_2|t2 1.718 0.125 13.767 0.000 1.718 1.077
Q7_4|t1 -0.455 0.081 -5.608 0.000 -0.455 -0.323
Q7_4|t2 0.911 0.087 10.467 0.000 0.911 0.646
Q7_5|t1 -0.661 0.101 -6.551 0.000 -0.661 -0.388
Q7_5|t2 1.220 0.107 11.362 0.000 1.220 0.717
Q7_7|t1 -1.343 0.116 -11.624 0.000 -1.343 -0.909
Q7_7|t2 -0.421 0.090 -4.696 0.000 -0.421 -0.285
Q7_8|t1 -0.546 0.086 -6.388 0.000 -0.546 -0.372
Q7_8|t2 1.103 0.093 11.889 0.000 1.103 0.750
Q7_14|t1 -1.257 0.095 -13.256 0.000 -1.257 -0.965
Q7_14|t2 -0.187 0.075 -2.503 0.012 -0.187 -0.144
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL 1.522 0.237 6.417 0.000 1.000 1.000
SC 1.171 0.229 5.119 0.000 1.000 1.000
IN 1.111 0.234 4.753 0.000 1.000 1.000
EN 1.545 0.275 5.620 0.000 1.000 1.000
.Q4_3 1.000 1.000 0.396
.Q4_4 1.000 1.000 0.239
.Q4_5 1.000 1.000 0.422
.Q4_9 1.000 1.000 0.530
.Q4_11 1.000 1.000 0.374
.Q4_15 1.000 1.000 0.399
.Q4_18 1.000 1.000 0.289
.Q5_1 1.000 1.000 0.461
.Q5_2 1.000 1.000 0.473
.Q5_3 1.000 1.000 0.490
.Q5_5 1.000 1.000 0.570
.Q5_6 1.000 1.000 0.363
.Q5_12 1.000 1.000 0.377
.Q6_2 1.000 1.000 0.474
.Q6_5 1.000 1.000 0.702
.Q6_6 1.000 1.000 0.237
.Q6_7 1.000 1.000 0.281
.Q6_8 1.000 1.000 0.371
.Q6_11 1.000 1.000 0.260
.Q7_2 1.000 1.000 0.393
.Q7_4 1.000 1.000 0.503
.Q7_5 1.000 1.000 0.345
.Q7_7 1.000 1.000 0.458
.Q7_8 1.000 1.000 0.463
.Q7_14 1.000 1.000 0.589
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Q4_3 0.630 0.630 1.000
Q4_4 0.489 0.489 1.000
Q4_5 0.650 0.650 1.000
Q4_9 0.728 0.728 1.000
Q4_11 0.612 0.612 1.000
Q4_15 0.632 0.632 1.000
Q4_18 0.537 0.537 1.000
Q5_1 0.679 0.679 1.000
Q5_2 0.688 0.688 1.000
Q5_3 0.700 0.700 1.000
Q5_5 0.755 0.755 1.000
Q5_6 0.603 0.603 1.000
Q5_12 0.614 0.614 1.000
Q6_2 0.688 0.688 1.000
Q6_5 0.838 0.838 1.000
Q6_6 0.486 0.486 1.000
Q6_7 0.530 0.530 1.000
Q6_8 0.609 0.609 1.000
Q6_11 0.510 0.510 1.000
Q7_2 0.627 0.627 1.000
Q7_4 0.709 0.709 1.000
Q7_5 0.587 0.587 1.000
Q7_7 0.677 0.677 1.000
Q7_8 0.680 0.680 1.000
Q7_14 0.768 0.768 1.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
rEL 0.889 0.013 68.903 0.000 0.920 0.824
rSC 0.863 0.020 42.191 0.000 0.878 0.785
rIN 0.908 0.016 56.391 0.000 0.915 0.801
rEN 0.825 0.022 37.817 0.000 0.871 0.781Model 2: Misclassification in IFA
Model details
cat(read_file(paste0(w.d, "/code/pools_study/model_misclass_ifa.txt")))model {
### Model
for(p in 1:N){
for(i in 1:nit){
# data model
y[p,i] ~ dcat(omega[p,i, ])
# LRV
ystar[p,i] ~ dnorm(lambda[i]*ksi[p,map[nit]], 1)
# Pr(nu = 3)
pi[p,i,3] = phi(ystar[p,i] - tau[i,2])
# Pr(nu = 2)
pi[p,i,2] = phi(ystar[p,i] - tau[i,1]) - phi(ystar[p,i] - tau[i,2])
# Pr(nu = 1)
pi[p,i,1] = 1 - phi(ystar[p,i] - tau[i,1])
# compute misclassificatication based prob
# observed category prob (Pr(y=c))
for(c in 1:ncat){
omega[p,i, c] = gamma[i,c,1]*pi[p,i,1] +
gamma[i,c,2]*pi[p,i,2] +
gamma[i,c,3]*pi[p,i,3]
}
}
}
### Priors
# misclassification
for(i in 1:nit){
for(c in 1:ncat){
gamma[i,c,1:ncat] ~ ddirch(xi*alpha[c,1:ncat])
}
}
# person parameters
for(p in 1:N){
#eta[p] ~ dnorm(0, 1) # latent ability
ksi[p, 1:M] ~ dmnorm(kappa[], inv.phi[,])
}
for(m in 1:M){
kappa[m] <- 0 # Means of latent variables
}
inv.phi[1:M,1:M] ~ dwish(dxphi.0[ , ], d); # prior for precision matrix for the latent variables
phi[1:M,1:M] <- inverse(inv.phi[ , ]); # the covariance matrix for the latent vars
for(m in 1:M){
for(mm in 1:M){
dxphi.0[m,mm] <- d*phi.0[m,mm];
}
}
# factor correlations
for(m in 1:M){
for(mm in 1:M){
phi.cor[m,mm] = (phi[m,mm])/((pow(phi[m,m], 0.5))*(pow(phi[mm,mm], 0.5)));
}
}
# priors for loadings
# loadings
lambda[1] = 1
lambda[8] = 1
lambda[13] = 1
lambda[19] = 1
for(i in 2:7){
lambda[i] ~ dnorm(0, 1)T(0,)
}
for(i in 9:12){
lambda[i] ~ dnorm(0, 1)T(0,)
}
for(i in 14:18){
lambda[i] ~ dnorm(0, 1)T(0,)
}
for(i in 20:25){
lambda[i] ~ dnorm(0, 1)T(0,)
}
for(i in 1:nit){
# Thresholds
tau[i, 1] = 0
tau[i, 2] ~ dnorm(0, 0.1)T(tau[i, 1],)
# LRV total variance
# total variance = residual variance + fact. Var.
theta[i] = 1 + pow(lambda[i],2)
# standardized loading
lambda.std[i] = lambda[i]/pow(theta[i],0.5)
}
# compute omega
lambda_sum1[1] = lambda[1]
lambda_sum2[1] = lambda[8]
lambda_sum3[1] = lambda[13]
lambda_sum4[1] = lambda[19]
for(i in 2:6){
#lambda_sum (sum factor loadings)
lambda_sum1[i] = lambda_sum1[i-1]+lambda[i]
lambda_sum2[i] = lambda_sum2[i-1]+lambda[i+7]
lambda_sum3[i] = lambda_sum3[i-1]+lambda[i+12]
lambda_sum4[i] = lambda_sum4[i-1]+lambda[i+18]
}
lambda_sum1[7] = lambda_sum1[6] + lambda[7]
# compute reliability
reli.omega[1] = (pow(lambda_sum1[7],2))/(pow(lambda_sum1[7],2)+7)
reli.omega[2] = (pow(lambda_sum2[6],2))/(pow(lambda_sum2[6],2)+6)
reli.omega[3] = (pow(lambda_sum3[6],2))/(pow(lambda_sum3[6],2)+6)
reli.omega[4] = (pow(lambda_sum4[6],2))/(pow(lambda_sum4[6],2)+6)
}Model results
# Save parameters
jags.params <- c("tau", "lambda", "theta", "reli.omega", "lambda.std",
"phi.cor", "inv.phi", "phi", "gamma")
# initial-values
jags.inits <- function(){
list(
"inv.phi"=solve(matrix(
c(1.52, 0.92, 0.97, 1.19,
0.92, 1.17, 0.74, 1.08,
0.97, 0.74, 1.11, 0.98,
1.19, 1.08, 0.98, 1.55), ncol=4, nrow=4, byrow=T
))
)
}
# data
jags.data <- list(
y = mydata,
N = nrow(mydata),
nit = ncol(mydata),
map = c(rep(1,7), rep(2,6), rep(3,6), rep(4,6)),
d = 8,
M = 4,
phi.0 = matrix(
c(1, 0.69, 0.75, 0.78,
0.69, 1, 0.65, 0.80,
0.75, 0.65, 1, 0.75,
0.78, 0.80, 0.75, 1), ncol=4, nrow=4, byrow=T
),
ncat = 3,
alpha = matrix(
c(0.90, 0.10, 0,
0.05, 0.90, 0.05,
0.0, 0.10, 0.90),
ncol=3, nrow=3, byrow=T
),
xi = 10
)
model.fit <- R2jags::jags(
model = paste0(w.d, "/code/pools_study/model_misclass_ifa.txt"),
parameters.to.save = jags.params,
inits = jags.inits,
data = jags.data,
n.chains = 4,
n.burnin = 5000,
n.iter = 10000
)module glm loadedCompiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12250
Unobserved stochastic nodes: 12862
Total graph size: 269608
Initializing modelprint(model.fit, width=1000)Inference for Bugs model at "C:/Users/noahp/Documents/GitHub/Padgett-Dissertation/code/pools_study/model_misclass_ifa.txt", fit using jags,
4 chains, each with 10000 iterations (first 5000 discarded), n.thin = 5
n.sims = 4000 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat n.eff
gamma[1,1,1] 0.767 0.061 0.638 0.729 0.770 0.810 0.877 1.00 1500
gamma[2,1,1] 0.841 0.049 0.733 0.809 0.845 0.876 0.929 1.01 390
gamma[3,1,1] 0.612 0.086 0.441 0.554 0.616 0.674 0.771 1.01 310
gamma[4,1,1] 0.522 0.088 0.353 0.461 0.522 0.580 0.693 1.02 140
gamma[5,1,1] 0.699 0.070 0.554 0.654 0.700 0.748 0.828 1.02 110
gamma[6,1,1] 0.721 0.076 0.566 0.671 0.723 0.773 0.866 1.01 210
gamma[7,1,1] 0.769 0.074 0.613 0.721 0.774 0.820 0.899 1.02 110
gamma[8,1,1] 0.698 0.078 0.534 0.647 0.701 0.753 0.838 1.00 4000
gamma[9,1,1] 0.894 0.101 0.607 0.850 0.925 0.970 0.997 1.00 1700
gamma[10,1,1] 0.805 0.123 0.533 0.724 0.823 0.901 0.987 1.00 1500
gamma[11,1,1] 0.263 0.069 0.146 0.214 0.256 0.307 0.413 1.01 190
gamma[12,1,1] 0.959 0.032 0.877 0.941 0.967 0.984 0.998 1.01 380
gamma[13,1,1] 0.962 0.032 0.882 0.945 0.970 0.987 0.999 1.00 770
gamma[14,1,1] 0.575 0.111 0.356 0.497 0.574 0.650 0.789 1.00 890
gamma[15,1,1] 0.820 0.129 0.538 0.732 0.847 0.925 0.993 1.01 460
gamma[16,1,1] 0.634 0.103 0.422 0.570 0.642 0.704 0.823 1.00 820
gamma[17,1,1] 0.692 0.098 0.491 0.626 0.697 0.763 0.870 1.01 320
gamma[18,1,1] 0.593 0.108 0.368 0.521 0.595 0.669 0.794 1.03 150
gamma[19,1,1] 0.953 0.033 0.873 0.933 0.960 0.978 0.998 1.00 2700
gamma[20,1,1] 0.907 0.047 0.808 0.878 0.910 0.940 0.988 1.02 130
gamma[21,1,1] 0.873 0.075 0.710 0.824 0.879 0.930 0.993 1.02 130
gamma[22,1,1] 0.975 0.022 0.920 0.964 0.981 0.991 0.999 1.00 1400
gamma[23,1,1] 0.192 0.053 0.107 0.155 0.185 0.223 0.315 1.01 610
gamma[24,1,1] 0.956 0.036 0.864 0.937 0.964 0.983 0.998 1.00 990
gamma[25,1,1] 0.152 0.042 0.080 0.123 0.149 0.177 0.245 1.01 340
gamma[1,2,1] 0.015 0.018 0.000 0.002 0.009 0.022 0.063 1.00 3700
gamma[2,2,1] 0.008 0.011 0.000 0.001 0.004 0.012 0.039 1.02 290
gamma[3,2,1] 0.005 0.007 0.000 0.001 0.002 0.007 0.023 1.03 240
gamma[4,2,1] 0.005 0.008 0.000 0.001 0.003 0.007 0.028 1.00 670
gamma[5,2,1] 0.015 0.013 0.000 0.005 0.012 0.022 0.049 1.03 400
gamma[6,2,1] 0.023 0.019 0.000 0.008 0.019 0.034 0.072 1.06 89
gamma[7,2,1] 0.007 0.009 0.000 0.001 0.003 0.009 0.031 1.04 130
gamma[8,2,1] 0.020 0.019 0.000 0.004 0.014 0.028 0.070 1.10 52
gamma[9,2,1] 0.018 0.024 0.000 0.002 0.009 0.025 0.089 1.01 960
gamma[10,2,1] 0.020 0.024 0.000 0.003 0.011 0.028 0.087 1.01 650
gamma[11,2,1] 0.035 0.031 0.000 0.013 0.029 0.050 0.114 1.22 44
gamma[12,2,1] 0.077 0.056 0.001 0.031 0.069 0.115 0.200 1.04 120
gamma[13,2,1] 0.051 0.038 0.001 0.022 0.044 0.072 0.140 1.05 110
gamma[14,2,1] 0.008 0.008 0.000 0.002 0.006 0.012 0.030 1.09 48
gamma[15,2,1] 0.007 0.011 0.000 0.001 0.003 0.010 0.039 1.01 480
gamma[16,2,1] 0.002 0.004 0.000 0.000 0.001 0.003 0.013 1.01 310
gamma[17,2,1] 0.005 0.006 0.000 0.000 0.002 0.006 0.023 1.17 36
gamma[18,2,1] 0.004 0.005 0.000 0.000 0.002 0.004 0.017 1.04 87
gamma[19,2,1] 0.014 0.019 0.000 0.002 0.007 0.020 0.069 1.01 390
gamma[20,2,1] 0.019 0.023 0.000 0.002 0.010 0.029 0.085 1.02 950
gamma[21,2,1] 0.022 0.028 0.000 0.003 0.011 0.031 0.102 1.02 280
gamma[22,2,1] 0.039 0.035 0.000 0.010 0.031 0.058 0.123 1.01 630
gamma[23,2,1] 0.075 0.039 0.018 0.047 0.068 0.095 0.169 1.01 240
gamma[24,2,1] 0.034 0.036 0.000 0.006 0.022 0.051 0.129 1.04 110
gamma[25,2,1] 0.038 0.028 0.002 0.018 0.034 0.052 0.108 1.07 70
gamma[1,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[2,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[3,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[4,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[5,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[6,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[7,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[8,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[9,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[10,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[11,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[12,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[13,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[14,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[15,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[16,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[17,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[18,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[19,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[20,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[21,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[22,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[23,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[24,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[25,3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[1,1,2] 0.233 0.061 0.123 0.190 0.230 0.271 0.362 1.00 1300
gamma[2,1,2] 0.159 0.049 0.071 0.124 0.155 0.191 0.267 1.01 380
gamma[3,1,2] 0.388 0.086 0.229 0.326 0.384 0.446 0.559 1.01 300
gamma[4,1,2] 0.478 0.088 0.307 0.420 0.478 0.539 0.647 1.03 110
gamma[5,1,2] 0.301 0.070 0.172 0.252 0.300 0.346 0.446 1.02 110
gamma[6,1,2] 0.279 0.076 0.134 0.227 0.277 0.329 0.434 1.02 180
gamma[7,1,2] 0.231 0.074 0.101 0.180 0.226 0.279 0.387 1.02 110
gamma[8,1,2] 0.302 0.078 0.162 0.247 0.299 0.353 0.466 1.00 4000
gamma[9,1,2] 0.106 0.101 0.003 0.030 0.075 0.150 0.393 1.01 1100
gamma[10,1,2] 0.195 0.123 0.013 0.099 0.177 0.276 0.467 1.01 1100
gamma[11,1,2] 0.737 0.069 0.587 0.693 0.744 0.786 0.854 1.02 160
gamma[12,1,2] 0.041 0.032 0.002 0.016 0.033 0.059 0.123 1.01 260
gamma[13,1,2] 0.038 0.032 0.001 0.013 0.030 0.055 0.118 1.00 800
gamma[14,1,2] 0.425 0.111 0.211 0.350 0.426 0.503 0.644 1.01 460
gamma[15,1,2] 0.180 0.129 0.007 0.075 0.153 0.268 0.462 1.01 290
gamma[16,1,2] 0.366 0.103 0.177 0.296 0.358 0.430 0.578 1.00 700
gamma[17,1,2] 0.308 0.098 0.130 0.237 0.303 0.374 0.509 1.01 490
gamma[18,1,2] 0.407 0.108 0.206 0.331 0.405 0.479 0.632 1.02 200
gamma[19,1,2] 0.047 0.033 0.002 0.022 0.040 0.067 0.127 1.01 1100
gamma[20,1,2] 0.093 0.047 0.012 0.060 0.090 0.122 0.192 1.08 90
gamma[21,1,2] 0.127 0.075 0.007 0.070 0.121 0.176 0.290 1.02 260
gamma[22,1,2] 0.025 0.022 0.001 0.009 0.019 0.036 0.080 1.00 980
gamma[23,1,2] 0.808 0.053 0.685 0.777 0.815 0.845 0.893 1.01 390
gamma[24,1,2] 0.044 0.036 0.002 0.017 0.036 0.063 0.136 1.01 470
gamma[25,1,2] 0.848 0.042 0.755 0.823 0.851 0.877 0.920 1.01 340
gamma[1,2,2] 0.946 0.049 0.817 0.925 0.962 0.982 0.998 1.00 4000
gamma[2,2,2] 0.951 0.049 0.820 0.930 0.967 0.987 0.999 1.00 1300
gamma[3,2,2] 0.696 0.118 0.476 0.608 0.690 0.779 0.935 1.02 170
gamma[4,2,2] 0.952 0.055 0.789 0.937 0.972 0.990 0.999 1.02 150
gamma[5,2,2] 0.916 0.076 0.715 0.880 0.941 0.973 0.995 1.02 180
gamma[6,2,2] 0.828 0.122 0.566 0.748 0.843 0.931 0.992 1.00 3500
gamma[7,2,2] 0.679 0.146 0.422 0.569 0.669 0.787 0.966 1.02 170
gamma[8,2,2] 0.932 0.059 0.780 0.906 0.950 0.976 0.997 1.01 280
gamma[9,2,2] 0.871 0.099 0.612 0.826 0.893 0.942 0.991 1.02 270
gamma[10,2,2] 0.953 0.043 0.837 0.933 0.965 0.985 0.998 1.00 2800
gamma[11,2,2] 0.647 0.050 0.549 0.614 0.645 0.680 0.745 1.01 330
gamma[12,2,2] 0.900 0.064 0.760 0.859 0.910 0.952 0.992 1.00 520
gamma[13,2,2] 0.928 0.046 0.824 0.900 0.935 0.963 0.994 1.01 450
gamma[14,2,2] 0.262 0.054 0.165 0.224 0.259 0.297 0.377 1.00 1100
gamma[15,2,2] 0.623 0.159 0.353 0.503 0.611 0.725 0.959 1.00 1900
gamma[16,2,2] 0.106 0.027 0.060 0.087 0.103 0.122 0.169 1.01 390
gamma[17,2,2] 0.372 0.084 0.222 0.312 0.368 0.425 0.549 1.01 460
gamma[18,2,2] 0.409 0.083 0.267 0.352 0.401 0.463 0.592 1.02 180
gamma[19,2,2] 0.971 0.027 0.900 0.960 0.979 0.991 0.999 1.02 370
gamma[20,2,2] 0.959 0.038 0.866 0.941 0.970 0.987 0.999 1.01 480
gamma[21,2,2] 0.963 0.034 0.869 0.948 0.973 0.988 0.999 1.01 630
gamma[22,2,2] 0.941 0.044 0.832 0.915 0.949 0.975 0.998 1.01 260
gamma[23,2,2] 0.738 0.042 0.650 0.711 0.739 0.766 0.818 1.00 1800
gamma[24,2,2] 0.945 0.046 0.825 0.921 0.957 0.980 0.998 1.01 420
gamma[25,2,2] 0.594 0.052 0.488 0.559 0.595 0.628 0.692 1.00 740
gamma[1,3,2] 0.051 0.023 0.012 0.034 0.048 0.066 0.102 1.00 860
gamma[2,3,2] 0.035 0.019 0.004 0.020 0.032 0.047 0.078 1.01 2900
gamma[3,3,2] 0.033 0.019 0.005 0.018 0.030 0.044 0.079 1.01 410
gamma[4,3,2] 0.171 0.060 0.048 0.132 0.171 0.211 0.291 1.07 57
gamma[5,3,2] 0.118 0.042 0.044 0.088 0.115 0.145 0.207 1.02 140
gamma[6,3,2] 0.041 0.024 0.005 0.024 0.038 0.056 0.095 1.03 240
gamma[7,3,2] 0.024 0.014 0.003 0.014 0.022 0.033 0.057 1.02 260
gamma[8,3,2] 0.230 0.055 0.129 0.192 0.228 0.265 0.345 1.00 1700
gamma[9,3,2] 0.328 0.163 0.028 0.207 0.334 0.451 0.628 1.01 370
gamma[10,3,2] 0.067 0.058 0.001 0.022 0.051 0.099 0.211 1.00 1700
gamma[11,3,2] 0.065 0.059 0.003 0.021 0.049 0.091 0.216 1.01 340
gamma[12,3,2] 0.345 0.079 0.192 0.290 0.344 0.399 0.501 1.00 890
gamma[13,3,2] 0.333 0.069 0.198 0.288 0.332 0.379 0.471 1.00 2300
gamma[14,3,2] 0.055 0.020 0.018 0.041 0.054 0.068 0.098 1.03 160
gamma[15,3,2] 0.105 0.088 0.003 0.033 0.079 0.162 0.308 1.01 590
gamma[16,3,2] 0.006 0.005 0.000 0.003 0.005 0.008 0.018 1.01 350
gamma[17,3,2] 0.032 0.016 0.005 0.020 0.030 0.041 0.066 1.01 810
gamma[18,3,2] 0.048 0.018 0.020 0.036 0.047 0.058 0.090 1.01 180
gamma[19,3,2] 0.127 0.057 0.026 0.084 0.124 0.164 0.242 1.04 110
gamma[20,3,2] 0.064 0.037 0.004 0.035 0.061 0.088 0.146 1.03 150
gamma[21,3,2] 0.294 0.100 0.088 0.226 0.298 0.367 0.476 1.03 110
gamma[22,3,2] 0.262 0.059 0.154 0.220 0.260 0.301 0.380 1.01 490
gamma[23,3,2] 0.057 0.053 0.002 0.018 0.040 0.082 0.197 1.01 850
gamma[24,3,2] 0.272 0.070 0.134 0.225 0.271 0.319 0.406 1.00 680
gamma[25,3,2] 0.069 0.063 0.002 0.022 0.052 0.098 0.229 1.00 780
gamma[1,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[2,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[3,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[4,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[5,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[6,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[7,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[8,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[9,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[10,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[11,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[12,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[13,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[14,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[15,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[16,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[17,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[18,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[19,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[20,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[21,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[22,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[23,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[24,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[25,1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
gamma[1,2,3] 0.038 0.046 0.000 0.005 0.022 0.056 0.156 1.01 570
gamma[2,2,3] 0.040 0.047 0.000 0.005 0.022 0.061 0.171 1.02 660
gamma[3,2,3] 0.299 0.117 0.062 0.216 0.305 0.385 0.519 1.01 290
gamma[4,2,3] 0.043 0.054 0.000 0.005 0.022 0.058 0.200 1.02 150
gamma[5,2,3] 0.069 0.074 0.000 0.012 0.044 0.103 0.262 1.10 63
gamma[6,2,3] 0.149 0.116 0.001 0.050 0.131 0.228 0.405 1.00 4000
gamma[7,2,3] 0.315 0.146 0.025 0.208 0.326 0.425 0.573 1.14 83
gamma[8,2,3] 0.048 0.053 0.000 0.008 0.030 0.069 0.193 1.00 550
gamma[9,2,3] 0.111 0.091 0.001 0.044 0.090 0.153 0.357 1.07 100
gamma[10,2,3] 0.027 0.035 0.000 0.002 0.013 0.039 0.128 1.02 220
gamma[11,2,3] 0.317 0.054 0.215 0.281 0.316 0.354 0.424 1.01 390
gamma[12,2,3] 0.023 0.027 0.000 0.003 0.013 0.035 0.090 1.05 100
gamma[13,2,3] 0.021 0.026 0.000 0.002 0.011 0.030 0.091 1.01 570
gamma[14,2,3] 0.729 0.053 0.616 0.696 0.732 0.766 0.827 1.00 1300
gamma[15,2,3] 0.370 0.159 0.035 0.267 0.383 0.491 0.642 1.04 360
gamma[16,2,3] 0.892 0.027 0.830 0.875 0.895 0.911 0.938 1.01 470
gamma[17,2,3] 0.623 0.083 0.448 0.570 0.628 0.682 0.771 1.01 480
gamma[18,2,3] 0.587 0.083 0.407 0.534 0.595 0.644 0.730 1.02 200
gamma[19,2,3] 0.015 0.019 0.000 0.002 0.008 0.020 0.065 1.01 310
gamma[20,2,3] 0.022 0.029 0.000 0.003 0.011 0.030 0.106 1.08 69
gamma[21,2,3] 0.014 0.020 0.000 0.001 0.007 0.019 0.071 1.01 310
gamma[22,2,3] 0.020 0.024 0.000 0.003 0.011 0.029 0.088 1.05 280
gamma[23,2,3] 0.187 0.046 0.104 0.157 0.186 0.217 0.283 1.01 210
gamma[24,2,3] 0.021 0.027 0.000 0.002 0.010 0.030 0.095 1.03 310
gamma[25,2,3] 0.368 0.058 0.258 0.328 0.366 0.406 0.487 1.00 1500
gamma[1,3,3] 0.949 0.023 0.898 0.934 0.952 0.966 0.988 1.00 1100
gamma[2,3,3] 0.965 0.019 0.922 0.953 0.968 0.980 0.996 1.00 1100
gamma[3,3,3] 0.967 0.019 0.921 0.956 0.970 0.982 0.995 1.00 650
gamma[4,3,3] 0.829 0.060 0.709 0.789 0.829 0.868 0.952 1.04 70
gamma[5,3,3] 0.882 0.042 0.793 0.855 0.885 0.912 0.956 1.02 160
gamma[6,3,3] 0.959 0.024 0.905 0.944 0.962 0.976 0.995 1.01 230
gamma[7,3,3] 0.976 0.014 0.943 0.967 0.978 0.986 0.997 1.01 430
gamma[8,3,3] 0.770 0.055 0.655 0.735 0.772 0.808 0.871 1.00 2300
gamma[9,3,3] 0.672 0.163 0.372 0.549 0.666 0.793 0.972 1.01 200
gamma[10,3,3] 0.933 0.058 0.789 0.901 0.949 0.978 0.999 1.00 1400
gamma[11,3,3] 0.935 0.059 0.784 0.909 0.951 0.979 0.997 1.01 340
gamma[12,3,3] 0.655 0.079 0.499 0.601 0.656 0.710 0.808 1.00 660
gamma[13,3,3] 0.667 0.069 0.529 0.621 0.668 0.712 0.802 1.00 2900
gamma[14,3,3] 0.945 0.020 0.902 0.932 0.946 0.959 0.982 1.01 220
gamma[15,3,3] 0.895 0.088 0.692 0.838 0.921 0.967 0.997 1.00 950
gamma[16,3,3] 0.994 0.005 0.982 0.992 0.995 0.997 1.000 1.00 630
gamma[17,3,3] 0.968 0.016 0.934 0.959 0.970 0.980 0.995 1.00 1100
gamma[18,3,3] 0.952 0.018 0.910 0.942 0.953 0.964 0.980 1.02 160
gamma[19,3,3] 0.873 0.057 0.758 0.836 0.876 0.916 0.974 1.02 100
gamma[20,3,3] 0.936 0.037 0.854 0.912 0.939 0.965 0.996 1.01 170
gamma[21,3,3] 0.706 0.100 0.524 0.633 0.702 0.774 0.912 1.03 87
gamma[22,3,3] 0.738 0.059 0.620 0.699 0.740 0.780 0.846 1.01 530
gamma[23,3,3] 0.943 0.053 0.803 0.918 0.960 0.982 0.998 1.01 1500
gamma[24,3,3] 0.728 0.070 0.594 0.681 0.729 0.775 0.866 1.00 590
gamma[25,3,3] 0.931 0.063 0.771 0.902 0.948 0.978 0.998 1.01 450
inv.phi[1,1] 3.368 1.621 0.951 2.208 3.123 4.248 7.213 1.09 35
inv.phi[2,1] -0.434 1.046 -2.551 -1.127 -0.389 0.292 1.487 1.02 170
inv.phi[3,1] -1.271 1.144 -3.926 -1.901 -1.155 -0.480 0.566 1.01 220
inv.phi[4,1] -1.270 1.197 -3.901 -1.972 -1.152 -0.480 0.762 1.01 240
inv.phi[1,2] -0.434 1.046 -2.551 -1.127 -0.389 0.292 1.487 1.02 170
inv.phi[2,2] 2.938 1.426 0.879 1.881 2.730 3.737 6.381 1.02 270
inv.phi[3,2] -0.254 1.008 -2.250 -0.892 -0.282 0.371 1.783 1.02 180
inv.phi[4,2] -1.826 1.412 -5.249 -2.569 -1.605 -0.826 0.326 1.02 150
inv.phi[1,3] -1.271 1.144 -3.926 -1.901 -1.155 -0.480 0.566 1.01 220
inv.phi[2,3] -0.254 1.008 -2.250 -0.892 -0.282 0.371 1.783 1.02 180
inv.phi[3,3] 2.641 1.417 0.638 1.597 2.371 3.383 6.162 1.02 260
inv.phi[4,3] -0.757 1.193 -3.407 -1.501 -0.619 0.110 1.213 1.01 320
inv.phi[1,4] -1.270 1.197 -3.901 -1.972 -1.152 -0.480 0.762 1.01 240
inv.phi[2,4] -1.826 1.412 -5.249 -2.569 -1.605 -0.826 0.326 1.02 150
inv.phi[3,4] -0.757 1.193 -3.407 -1.501 -0.619 0.110 1.213 1.01 320
inv.phi[4,4] 3.537 2.014 0.764 2.051 3.114 4.649 8.520 1.03 120
lambda[1] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
lambda[2] 1.367 0.226 1.000 1.197 1.335 1.524 1.827 1.03 110
lambda[3] 1.251 0.209 0.876 1.105 1.236 1.388 1.675 1.02 130
lambda[4] 1.152 0.224 0.736 1.000 1.145 1.304 1.612 1.06 50
lambda[5] 1.475 0.315 0.935 1.254 1.462 1.657 2.203 1.08 39
lambda[6] 1.205 0.244 0.812 1.023 1.171 1.367 1.731 1.09 35
lambda[7] 1.450 0.255 0.933 1.290 1.441 1.618 1.956 1.09 33
lambda[8] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
lambda[9] 0.708 0.094 0.536 0.645 0.705 0.768 0.908 1.01 180
lambda[10] 0.588 0.083 0.446 0.533 0.581 0.638 0.763 1.02 160
lambda[11] 0.614 0.083 0.452 0.557 0.613 0.668 0.779 1.01 1000
lambda[12] 0.940 0.141 0.686 0.838 0.933 1.034 1.238 1.03 78
lambda[13] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
lambda[14] 1.130 0.268 0.632 0.949 1.124 1.287 1.699 1.07 44
lambda[15] 0.439 0.096 0.298 0.373 0.422 0.486 0.671 1.01 310
lambda[16] 1.275 0.233 0.859 1.105 1.258 1.432 1.769 1.13 25
lambda[17] 1.241 0.243 0.793 1.065 1.228 1.405 1.729 1.08 39
lambda[18] 1.364 0.290 0.887 1.156 1.337 1.527 2.031 1.05 160
lambda[19] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
lambda[20] 1.029 0.151 0.757 0.927 1.020 1.120 1.365 1.07 43
lambda[21] 0.870 0.140 0.634 0.765 0.858 0.963 1.162 1.08 42
lambda[22] 1.120 0.142 0.868 1.021 1.110 1.205 1.430 1.02 180
lambda[23] 0.780 0.101 0.591 0.708 0.776 0.843 0.986 1.01 300
lambda[24] 0.867 0.118 0.657 0.787 0.859 0.935 1.131 1.02 140
lambda[25] 0.711 0.096 0.536 0.644 0.708 0.774 0.907 1.01 290
lambda.std[1] 0.707 0.000 0.707 0.707 0.707 0.707 0.707 1.00 1
lambda.std[2] 0.800 0.046 0.707 0.767 0.800 0.836 0.877 1.03 97
lambda.std[3] 0.773 0.052 0.659 0.742 0.777 0.811 0.859 1.02 120
lambda.std[4] 0.745 0.066 0.593 0.707 0.753 0.794 0.850 1.07 47
lambda.std[5] 0.816 0.058 0.683 0.782 0.825 0.856 0.911 1.09 38
lambda.std[6] 0.759 0.062 0.630 0.715 0.760 0.807 0.866 1.08 38
lambda.std[7] 0.815 0.051 0.682 0.790 0.822 0.851 0.890 1.08 39
lambda.std[8] 0.707 0.000 0.707 0.707 0.707 0.707 0.707 1.00 1
lambda.std[9] 0.574 0.051 0.473 0.542 0.576 0.609 0.672 1.01 190
lambda.std[10] 0.504 0.052 0.407 0.470 0.503 0.538 0.607 1.02 160
lambda.std[11] 0.520 0.051 0.412 0.487 0.522 0.556 0.615 1.01 880
lambda.std[12] 0.679 0.055 0.566 0.642 0.682 0.719 0.778 1.03 82
lambda.std[13] 0.707 0.000 0.707 0.707 0.707 0.707 0.707 1.00 1
lambda.std[14] 0.734 0.082 0.534 0.688 0.747 0.790 0.862 1.07 44
lambda.std[15] 0.398 0.070 0.285 0.349 0.389 0.437 0.557 1.01 290
lambda.std[16] 0.778 0.056 0.652 0.741 0.783 0.820 0.870 1.13 26
lambda.std[17] 0.768 0.064 0.621 0.729 0.775 0.815 0.866 1.06 48
lambda.std[18] 0.795 0.060 0.663 0.756 0.801 0.837 0.897 1.03 370
lambda.std[19] 0.707 0.000 0.707 0.707 0.707 0.707 0.707 1.00 1
lambda.std[20] 0.712 0.051 0.604 0.680 0.714 0.746 0.807 1.07 42
lambda.std[21] 0.650 0.059 0.536 0.608 0.651 0.694 0.758 1.07 46
lambda.std[22] 0.742 0.042 0.655 0.714 0.743 0.770 0.820 1.02 180
lambda.std[23] 0.611 0.049 0.509 0.578 0.613 0.645 0.702 1.01 310
lambda.std[24] 0.651 0.050 0.549 0.619 0.652 0.683 0.749 1.02 150
lambda.std[25] 0.576 0.052 0.473 0.542 0.578 0.612 0.672 1.01 290
phi[1,1] 3.603 2.298 1.010 2.176 3.039 4.428 9.327 1.08 49
phi[2,1] 3.059 1.887 0.754 1.859 2.687 3.751 7.649 1.06 58
phi[3,1] 3.117 2.247 0.498 1.740 2.621 3.797 9.606 1.11 52
phi[4,1] 3.399 1.320 1.151 2.559 3.257 4.097 6.377 1.10 49
phi[1,2] 3.059 1.887 0.754 1.859 2.687 3.751 7.649 1.06 58
phi[2,2] 4.064 2.455 1.060 2.296 3.487 5.147 10.603 1.04 67
phi[3,2] 3.074 2.152 0.374 1.607 2.594 4.017 9.025 1.13 35
phi[4,2] 3.673 1.507 1.041 2.686 3.529 4.491 7.169 1.06 50
phi[1,3] 3.117 2.247 0.498 1.740 2.621 3.797 9.606 1.11 52
phi[2,3] 3.074 2.152 0.374 1.607 2.594 4.017 9.025 1.13 35
phi[3,3] 4.037 2.948 0.836 2.032 3.281 4.972 12.539 1.09 39
phi[4,3] 3.327 1.665 0.208 2.156 3.261 4.303 7.106 1.10 37
phi[1,4] 3.399 1.320 1.151 2.559 3.257 4.097 6.377 1.10 49
phi[2,4] 3.673 1.507 1.041 2.686 3.529 4.491 7.169 1.06 50
phi[3,4] 3.327 1.665 0.208 2.156 3.261 4.303 7.106 1.10 37
phi[4,4] 4.476 0.675 3.437 4.023 4.361 4.815 6.150 1.06 87
phi.cor[1,1] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
phi.cor[2,1] 0.815 0.135 0.441 0.759 0.857 0.910 0.964 1.04 140
phi.cor[3,1] 0.821 0.178 0.331 0.782 0.877 0.928 0.975 1.08 110
phi.cor[4,1] 0.864 0.126 0.470 0.842 0.908 0.938 0.970 1.06 84
phi.cor[1,2] 0.815 0.135 0.441 0.759 0.857 0.910 0.964 1.04 140
phi.cor[2,2] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
phi.cor[3,2] 0.769 0.195 0.216 0.715 0.834 0.896 0.967 1.06 94
phi.cor[4,2] 0.873 0.156 0.462 0.862 0.922 0.950 0.976 1.21 45
phi.cor[1,3] 0.821 0.178 0.331 0.782 0.877 0.928 0.975 1.08 110
phi.cor[2,3] 0.769 0.195 0.216 0.715 0.834 0.896 0.967 1.06 94
phi.cor[3,3] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
phi.cor[4,3] 0.795 0.218 0.100 0.751 0.880 0.931 0.968 1.07 73
phi.cor[1,4] 0.864 0.126 0.470 0.842 0.908 0.938 0.970 1.06 84
phi.cor[2,4] 0.873 0.156 0.462 0.862 0.922 0.950 0.976 1.21 45
phi.cor[3,4] 0.795 0.218 0.100 0.751 0.880 0.931 0.968 1.07 73
phi.cor[4,4] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
reli.omega[1] 0.917 0.016 0.882 0.909 0.918 0.927 0.943 1.12 28
reli.omega[2] 0.796 0.018 0.756 0.784 0.797 0.809 0.826 1.04 83
reli.omega[3] 0.872 0.021 0.827 0.857 0.873 0.888 0.906 1.07 47
reli.omega[4] 0.841 0.018 0.799 0.830 0.842 0.853 0.873 1.04 80
tau[1,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[2,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[4,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[5,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[6,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[7,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[8,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[9,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[10,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[11,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[12,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[13,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[14,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[15,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[16,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[17,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[18,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[19,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[20,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[21,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[22,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[23,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[24,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[25,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.00 1
tau[1,2] 3.889 0.324 3.329 3.670 3.859 4.087 4.617 1.03 140
tau[2,2] 4.801 0.716 3.673 4.239 4.703 5.325 6.255 1.02 110
tau[3,2] 4.220 0.577 3.253 3.808 4.164 4.589 5.453 1.01 230
tau[4,2] 3.511 0.722 2.253 2.987 3.490 3.979 5.021 1.07 43
tau[5,2] 4.637 0.972 3.082 3.881 4.593 5.259 6.692 1.06 49
tau[6,2] 4.170 0.732 3.031 3.629 4.067 4.608 5.786 1.07 51
tau[7,2] 5.432 0.764 4.023 4.908 5.384 5.927 7.034 1.06 47
tau[8,2] 3.099 0.325 2.532 2.870 3.076 3.304 3.804 1.03 100
tau[9,2] 1.132 0.251 0.643 0.982 1.128 1.280 1.634 1.01 320
tau[10,2] 1.539 0.155 1.287 1.433 1.523 1.626 1.878 1.00 3600
tau[11,2] 0.056 0.046 0.002 0.019 0.045 0.081 0.171 1.00 4000
tau[12,2] 2.690 0.458 1.988 2.366 2.623 2.932 3.807 1.03 90
tau[13,2] 2.758 0.288 2.245 2.554 2.737 2.939 3.371 1.01 230
tau[14,2] 4.573 0.863 3.093 3.968 4.498 5.108 6.466 1.06 49
tau[15,2] 1.722 0.566 1.200 1.400 1.555 1.839 3.081 1.02 820
tau[16,2] 5.510 0.703 4.277 4.993 5.460 5.987 7.001 1.08 35
tau[17,2] 4.676 0.741 3.365 4.152 4.626 5.147 6.250 1.05 62
tau[18,2] 5.255 0.865 3.841 4.684 5.193 5.681 7.469 1.08 68
tau[19,2] 2.694 0.264 2.249 2.509 2.665 2.847 3.308 1.04 78
tau[20,2] 3.450 0.509 2.655 3.077 3.382 3.762 4.636 1.04 62
tau[21,2] 2.429 0.480 1.715 2.062 2.343 2.741 3.488 1.06 48
tau[22,2] 3.267 0.431 2.558 2.958 3.222 3.529 4.234 1.02 120
tau[23,2] 0.042 0.036 0.001 0.014 0.033 0.060 0.135 1.00 4000
tau[24,2] 2.854 0.411 2.184 2.562 2.810 3.087 3.812 1.01 270
tau[25,2] 0.033 0.030 0.001 0.011 0.025 0.048 0.109 1.00 3500
theta[1] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.00 1
theta[2] 2.919 0.645 2.001 2.432 2.782 3.323 4.338 1.03 110
theta[3] 2.608 0.543 1.768 2.221 2.528 2.927 3.807 1.02 140
theta[4] 2.378 0.528 1.542 2.000 2.311 2.702 3.600 1.06 54
theta[5] 3.274 0.995 1.874 2.572 3.136 3.746 5.854 1.08 40
theta[6] 2.512 0.626 1.660 2.046 2.370 2.868 3.996 1.10 34
theta[7] 3.168 0.750 1.870 2.665 3.076 3.618 4.827 1.10 30
theta[8] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.00 1
theta[9] 1.510 0.136 1.288 1.416 1.497 1.589 1.825 1.01 180
theta[10] 1.353 0.102 1.199 1.284 1.338 1.407 1.583 1.02 160
theta[11] 1.384 0.103 1.205 1.310 1.375 1.447 1.607 1.00 1600
theta[12] 1.903 0.273 1.470 1.702 1.871 2.069 2.533 1.04 74
theta[13] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.00 1
theta[14] 2.350 0.641 1.399 1.900 2.264 2.655 3.887 1.06 45
theta[15] 1.202 0.095 1.089 1.139 1.178 1.236 1.451 1.01 410
theta[16] 2.681 0.618 1.738 2.220 2.583 3.051 4.128 1.13 25
theta[17] 2.598 0.619 1.629 2.133 2.507 2.974 3.989 1.09 34
theta[18] 2.944 0.852 1.786 2.336 2.787 3.331 5.127 1.06 110
theta[19] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.00 1
theta[20] 2.082 0.321 1.574 1.859 2.039 2.255 2.862 1.06 44
theta[21] 1.776 0.252 1.402 1.585 1.736 1.928 2.350 1.08 39
theta[22] 2.274 0.327 1.753 2.042 2.233 2.452 3.045 1.02 190
theta[23] 1.618 0.160 1.349 1.502 1.602 1.711 1.973 1.01 280
theta[24] 1.765 0.211 1.431 1.620 1.738 1.874 2.278 1.02 130
theta[25] 1.515 0.139 1.288 1.415 1.501 1.599 1.823 1.01 310
deviance 16276.867 121.949 16038.854 16194.304 16276.698 16358.334 16513.894 1.01 500
For each parameter, n.eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
DIC info (using the rule, pD = var(deviance)/2)
pD = 7397.0 and DIC = 23673.9
DIC is an estimate of expected predictive error (lower deviance is better).kable(model.fit$BUGSoutput$summary, format="html", digits=3) %>%
kable_styling(full_width = T) %>%
scroll_box(width="100%", height="500px")| mean | sd | 2.5% | 25% | 50% | 75% | 97.5% | Rhat | n.eff | |
|---|---|---|---|---|---|---|---|---|---|
| deviance | 16276.867 | 121.949 | 16038.854 | 16194.304 | 16276.698 | 16358.334 | 16513.894 | 1.01 | 500 |
| gamma[1,1,1] | 0.767 | 0.061 | 0.638 | 0.729 | 0.770 | 0.810 | 0.877 | 1.00 | 1500 |
| gamma[2,1,1] | 0.841 | 0.049 | 0.733 | 0.809 | 0.845 | 0.876 | 0.929 | 1.01 | 390 |
| gamma[3,1,1] | 0.612 | 0.086 | 0.441 | 0.554 | 0.616 | 0.674 | 0.771 | 1.01 | 310 |
| gamma[4,1,1] | 0.522 | 0.088 | 0.353 | 0.461 | 0.522 | 0.580 | 0.693 | 1.02 | 140 |
| gamma[5,1,1] | 0.699 | 0.070 | 0.554 | 0.654 | 0.700 | 0.748 | 0.828 | 1.02 | 110 |
| gamma[6,1,1] | 0.721 | 0.076 | 0.566 | 0.671 | 0.723 | 0.773 | 0.866 | 1.01 | 210 |
| gamma[7,1,1] | 0.769 | 0.074 | 0.613 | 0.721 | 0.774 | 0.820 | 0.899 | 1.02 | 110 |
| gamma[8,1,1] | 0.698 | 0.078 | 0.534 | 0.647 | 0.701 | 0.753 | 0.838 | 1.00 | 4000 |
| gamma[9,1,1] | 0.894 | 0.101 | 0.607 | 0.850 | 0.925 | 0.970 | 0.997 | 1.00 | 1700 |
| gamma[10,1,1] | 0.805 | 0.123 | 0.533 | 0.724 | 0.823 | 0.901 | 0.987 | 1.00 | 1500 |
| gamma[11,1,1] | 0.263 | 0.069 | 0.146 | 0.214 | 0.256 | 0.307 | 0.413 | 1.01 | 190 |
| gamma[12,1,1] | 0.959 | 0.032 | 0.877 | 0.941 | 0.967 | 0.984 | 0.998 | 1.01 | 380 |
| gamma[13,1,1] | 0.962 | 0.032 | 0.882 | 0.945 | 0.970 | 0.987 | 0.999 | 1.00 | 770 |
| gamma[14,1,1] | 0.575 | 0.111 | 0.356 | 0.497 | 0.574 | 0.650 | 0.789 | 1.00 | 890 |
| gamma[15,1,1] | 0.820 | 0.129 | 0.538 | 0.732 | 0.847 | 0.925 | 0.993 | 1.01 | 460 |
| gamma[16,1,1] | 0.634 | 0.103 | 0.422 | 0.570 | 0.642 | 0.704 | 0.823 | 1.00 | 820 |
| gamma[17,1,1] | 0.692 | 0.098 | 0.491 | 0.626 | 0.697 | 0.763 | 0.870 | 1.01 | 320 |
| gamma[18,1,1] | 0.593 | 0.108 | 0.368 | 0.521 | 0.595 | 0.669 | 0.794 | 1.03 | 150 |
| gamma[19,1,1] | 0.953 | 0.033 | 0.873 | 0.933 | 0.960 | 0.978 | 0.998 | 1.00 | 2700 |
| gamma[20,1,1] | 0.907 | 0.047 | 0.808 | 0.878 | 0.910 | 0.940 | 0.988 | 1.02 | 130 |
| gamma[21,1,1] | 0.873 | 0.075 | 0.710 | 0.824 | 0.879 | 0.930 | 0.993 | 1.02 | 130 |
| gamma[22,1,1] | 0.975 | 0.022 | 0.920 | 0.964 | 0.981 | 0.991 | 0.999 | 1.00 | 1400 |
| gamma[23,1,1] | 0.192 | 0.053 | 0.107 | 0.155 | 0.185 | 0.223 | 0.315 | 1.01 | 610 |
| gamma[24,1,1] | 0.956 | 0.036 | 0.864 | 0.937 | 0.964 | 0.983 | 0.998 | 1.00 | 990 |
| gamma[25,1,1] | 0.152 | 0.042 | 0.080 | 0.123 | 0.149 | 0.177 | 0.245 | 1.01 | 340 |
| gamma[1,2,1] | 0.015 | 0.018 | 0.000 | 0.002 | 0.009 | 0.022 | 0.063 | 1.00 | 3700 |
| gamma[2,2,1] | 0.008 | 0.011 | 0.000 | 0.001 | 0.004 | 0.012 | 0.039 | 1.02 | 290 |
| gamma[3,2,1] | 0.005 | 0.007 | 0.000 | 0.001 | 0.002 | 0.007 | 0.023 | 1.03 | 240 |
| gamma[4,2,1] | 0.005 | 0.008 | 0.000 | 0.001 | 0.003 | 0.007 | 0.028 | 1.00 | 670 |
| gamma[5,2,1] | 0.015 | 0.013 | 0.000 | 0.005 | 0.012 | 0.022 | 0.049 | 1.03 | 400 |
| gamma[6,2,1] | 0.023 | 0.019 | 0.000 | 0.008 | 0.019 | 0.034 | 0.072 | 1.06 | 89 |
| gamma[7,2,1] | 0.007 | 0.009 | 0.000 | 0.001 | 0.003 | 0.009 | 0.031 | 1.04 | 130 |
| gamma[8,2,1] | 0.020 | 0.019 | 0.000 | 0.004 | 0.014 | 0.028 | 0.070 | 1.10 | 52 |
| gamma[9,2,1] | 0.018 | 0.024 | 0.000 | 0.002 | 0.009 | 0.025 | 0.089 | 1.01 | 960 |
| gamma[10,2,1] | 0.020 | 0.024 | 0.000 | 0.003 | 0.011 | 0.028 | 0.087 | 1.01 | 650 |
| gamma[11,2,1] | 0.035 | 0.031 | 0.000 | 0.013 | 0.029 | 0.050 | 0.114 | 1.22 | 44 |
| gamma[12,2,1] | 0.077 | 0.056 | 0.001 | 0.031 | 0.069 | 0.115 | 0.200 | 1.04 | 120 |
| gamma[13,2,1] | 0.051 | 0.038 | 0.001 | 0.022 | 0.044 | 0.072 | 0.140 | 1.05 | 110 |
| gamma[14,2,1] | 0.008 | 0.008 | 0.000 | 0.002 | 0.006 | 0.012 | 0.030 | 1.09 | 48 |
| gamma[15,2,1] | 0.007 | 0.011 | 0.000 | 0.001 | 0.003 | 0.010 | 0.039 | 1.01 | 480 |
| gamma[16,2,1] | 0.002 | 0.004 | 0.000 | 0.000 | 0.001 | 0.003 | 0.013 | 1.01 | 310 |
| gamma[17,2,1] | 0.005 | 0.006 | 0.000 | 0.000 | 0.002 | 0.006 | 0.023 | 1.17 | 36 |
| gamma[18,2,1] | 0.004 | 0.005 | 0.000 | 0.000 | 0.002 | 0.004 | 0.017 | 1.04 | 87 |
| gamma[19,2,1] | 0.014 | 0.019 | 0.000 | 0.002 | 0.007 | 0.020 | 0.069 | 1.01 | 390 |
| gamma[20,2,1] | 0.019 | 0.023 | 0.000 | 0.002 | 0.010 | 0.029 | 0.085 | 1.02 | 950 |
| gamma[21,2,1] | 0.022 | 0.028 | 0.000 | 0.003 | 0.011 | 0.031 | 0.102 | 1.02 | 280 |
| gamma[22,2,1] | 0.039 | 0.035 | 0.000 | 0.010 | 0.031 | 0.058 | 0.123 | 1.01 | 630 |
| gamma[23,2,1] | 0.075 | 0.039 | 0.018 | 0.047 | 0.068 | 0.095 | 0.169 | 1.01 | 240 |
| gamma[24,2,1] | 0.034 | 0.036 | 0.000 | 0.006 | 0.022 | 0.051 | 0.129 | 1.04 | 110 |
| gamma[25,2,1] | 0.038 | 0.028 | 0.002 | 0.018 | 0.034 | 0.052 | 0.108 | 1.07 | 70 |
| gamma[1,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[2,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[3,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[4,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[5,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[6,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[7,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[8,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[9,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[10,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[11,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[12,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[13,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[14,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[15,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[16,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[17,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[18,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[19,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[20,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[21,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[22,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[23,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[24,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[25,3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[1,1,2] | 0.233 | 0.061 | 0.123 | 0.190 | 0.230 | 0.271 | 0.362 | 1.00 | 1300 |
| gamma[2,1,2] | 0.159 | 0.049 | 0.071 | 0.124 | 0.155 | 0.191 | 0.267 | 1.01 | 380 |
| gamma[3,1,2] | 0.388 | 0.086 | 0.229 | 0.326 | 0.384 | 0.446 | 0.559 | 1.01 | 300 |
| gamma[4,1,2] | 0.478 | 0.088 | 0.307 | 0.420 | 0.478 | 0.539 | 0.647 | 1.03 | 110 |
| gamma[5,1,2] | 0.301 | 0.070 | 0.172 | 0.252 | 0.300 | 0.346 | 0.446 | 1.02 | 110 |
| gamma[6,1,2] | 0.279 | 0.076 | 0.134 | 0.227 | 0.277 | 0.329 | 0.434 | 1.02 | 180 |
| gamma[7,1,2] | 0.231 | 0.074 | 0.101 | 0.180 | 0.226 | 0.279 | 0.387 | 1.02 | 110 |
| gamma[8,1,2] | 0.302 | 0.078 | 0.162 | 0.247 | 0.299 | 0.353 | 0.466 | 1.00 | 4000 |
| gamma[9,1,2] | 0.106 | 0.101 | 0.003 | 0.030 | 0.075 | 0.150 | 0.393 | 1.01 | 1100 |
| gamma[10,1,2] | 0.195 | 0.123 | 0.013 | 0.099 | 0.177 | 0.276 | 0.467 | 1.01 | 1100 |
| gamma[11,1,2] | 0.737 | 0.069 | 0.587 | 0.693 | 0.744 | 0.786 | 0.854 | 1.02 | 160 |
| gamma[12,1,2] | 0.041 | 0.032 | 0.002 | 0.016 | 0.033 | 0.059 | 0.123 | 1.01 | 260 |
| gamma[13,1,2] | 0.038 | 0.032 | 0.001 | 0.013 | 0.030 | 0.055 | 0.118 | 1.00 | 800 |
| gamma[14,1,2] | 0.425 | 0.111 | 0.211 | 0.350 | 0.426 | 0.503 | 0.644 | 1.01 | 460 |
| gamma[15,1,2] | 0.180 | 0.129 | 0.007 | 0.075 | 0.153 | 0.268 | 0.462 | 1.01 | 290 |
| gamma[16,1,2] | 0.366 | 0.103 | 0.177 | 0.296 | 0.358 | 0.430 | 0.578 | 1.00 | 700 |
| gamma[17,1,2] | 0.308 | 0.098 | 0.130 | 0.237 | 0.303 | 0.374 | 0.509 | 1.01 | 490 |
| gamma[18,1,2] | 0.407 | 0.108 | 0.206 | 0.331 | 0.405 | 0.479 | 0.632 | 1.02 | 200 |
| gamma[19,1,2] | 0.047 | 0.033 | 0.002 | 0.022 | 0.040 | 0.067 | 0.127 | 1.01 | 1100 |
| gamma[20,1,2] | 0.093 | 0.047 | 0.012 | 0.060 | 0.090 | 0.122 | 0.192 | 1.08 | 90 |
| gamma[21,1,2] | 0.127 | 0.075 | 0.007 | 0.070 | 0.121 | 0.176 | 0.290 | 1.02 | 260 |
| gamma[22,1,2] | 0.025 | 0.022 | 0.001 | 0.009 | 0.019 | 0.036 | 0.080 | 1.00 | 980 |
| gamma[23,1,2] | 0.808 | 0.053 | 0.685 | 0.777 | 0.815 | 0.845 | 0.893 | 1.01 | 390 |
| gamma[24,1,2] | 0.044 | 0.036 | 0.002 | 0.017 | 0.036 | 0.063 | 0.136 | 1.01 | 470 |
| gamma[25,1,2] | 0.848 | 0.042 | 0.755 | 0.823 | 0.851 | 0.877 | 0.920 | 1.01 | 340 |
| gamma[1,2,2] | 0.946 | 0.049 | 0.817 | 0.925 | 0.962 | 0.982 | 0.998 | 1.00 | 4000 |
| gamma[2,2,2] | 0.951 | 0.049 | 0.820 | 0.930 | 0.967 | 0.987 | 0.999 | 1.00 | 1300 |
| gamma[3,2,2] | 0.696 | 0.118 | 0.476 | 0.608 | 0.690 | 0.779 | 0.935 | 1.02 | 170 |
| gamma[4,2,2] | 0.952 | 0.055 | 0.789 | 0.937 | 0.972 | 0.990 | 0.999 | 1.02 | 150 |
| gamma[5,2,2] | 0.916 | 0.076 | 0.715 | 0.880 | 0.941 | 0.973 | 0.995 | 1.02 | 180 |
| gamma[6,2,2] | 0.828 | 0.122 | 0.566 | 0.748 | 0.843 | 0.931 | 0.992 | 1.00 | 3500 |
| gamma[7,2,2] | 0.679 | 0.146 | 0.422 | 0.569 | 0.669 | 0.787 | 0.966 | 1.02 | 170 |
| gamma[8,2,2] | 0.932 | 0.059 | 0.780 | 0.906 | 0.950 | 0.976 | 0.997 | 1.01 | 280 |
| gamma[9,2,2] | 0.871 | 0.099 | 0.612 | 0.826 | 0.893 | 0.942 | 0.991 | 1.02 | 270 |
| gamma[10,2,2] | 0.953 | 0.043 | 0.837 | 0.933 | 0.965 | 0.985 | 0.998 | 1.00 | 2800 |
| gamma[11,2,2] | 0.647 | 0.050 | 0.549 | 0.614 | 0.645 | 0.680 | 0.745 | 1.01 | 330 |
| gamma[12,2,2] | 0.900 | 0.064 | 0.760 | 0.859 | 0.910 | 0.952 | 0.992 | 1.00 | 520 |
| gamma[13,2,2] | 0.928 | 0.046 | 0.824 | 0.900 | 0.935 | 0.963 | 0.994 | 1.01 | 450 |
| gamma[14,2,2] | 0.262 | 0.054 | 0.165 | 0.224 | 0.259 | 0.297 | 0.377 | 1.00 | 1100 |
| gamma[15,2,2] | 0.623 | 0.159 | 0.353 | 0.503 | 0.611 | 0.725 | 0.959 | 1.00 | 1900 |
| gamma[16,2,2] | 0.106 | 0.027 | 0.060 | 0.087 | 0.103 | 0.122 | 0.169 | 1.01 | 390 |
| gamma[17,2,2] | 0.372 | 0.084 | 0.222 | 0.312 | 0.368 | 0.425 | 0.549 | 1.01 | 460 |
| gamma[18,2,2] | 0.409 | 0.083 | 0.267 | 0.352 | 0.401 | 0.463 | 0.592 | 1.02 | 180 |
| gamma[19,2,2] | 0.971 | 0.027 | 0.900 | 0.960 | 0.979 | 0.991 | 0.999 | 1.02 | 370 |
| gamma[20,2,2] | 0.959 | 0.038 | 0.866 | 0.941 | 0.970 | 0.987 | 0.999 | 1.01 | 480 |
| gamma[21,2,2] | 0.963 | 0.034 | 0.869 | 0.948 | 0.973 | 0.988 | 0.999 | 1.01 | 630 |
| gamma[22,2,2] | 0.941 | 0.044 | 0.832 | 0.915 | 0.949 | 0.975 | 0.998 | 1.01 | 260 |
| gamma[23,2,2] | 0.738 | 0.042 | 0.650 | 0.711 | 0.739 | 0.766 | 0.818 | 1.00 | 1800 |
| gamma[24,2,2] | 0.945 | 0.046 | 0.825 | 0.921 | 0.957 | 0.980 | 0.998 | 1.01 | 420 |
| gamma[25,2,2] | 0.594 | 0.052 | 0.488 | 0.559 | 0.595 | 0.628 | 0.692 | 1.00 | 740 |
| gamma[1,3,2] | 0.051 | 0.023 | 0.012 | 0.034 | 0.048 | 0.066 | 0.102 | 1.00 | 860 |
| gamma[2,3,2] | 0.035 | 0.019 | 0.004 | 0.020 | 0.032 | 0.047 | 0.078 | 1.01 | 2900 |
| gamma[3,3,2] | 0.033 | 0.019 | 0.005 | 0.018 | 0.030 | 0.044 | 0.079 | 1.01 | 410 |
| gamma[4,3,2] | 0.171 | 0.060 | 0.048 | 0.132 | 0.171 | 0.211 | 0.291 | 1.07 | 57 |
| gamma[5,3,2] | 0.118 | 0.042 | 0.044 | 0.088 | 0.115 | 0.145 | 0.207 | 1.02 | 140 |
| gamma[6,3,2] | 0.041 | 0.024 | 0.005 | 0.024 | 0.038 | 0.056 | 0.095 | 1.03 | 240 |
| gamma[7,3,2] | 0.024 | 0.014 | 0.003 | 0.014 | 0.022 | 0.033 | 0.057 | 1.02 | 260 |
| gamma[8,3,2] | 0.230 | 0.055 | 0.129 | 0.192 | 0.228 | 0.265 | 0.345 | 1.00 | 1700 |
| gamma[9,3,2] | 0.328 | 0.163 | 0.028 | 0.207 | 0.334 | 0.451 | 0.628 | 1.01 | 370 |
| gamma[10,3,2] | 0.067 | 0.058 | 0.001 | 0.022 | 0.051 | 0.099 | 0.211 | 1.00 | 1700 |
| gamma[11,3,2] | 0.065 | 0.059 | 0.003 | 0.021 | 0.049 | 0.091 | 0.216 | 1.01 | 340 |
| gamma[12,3,2] | 0.345 | 0.079 | 0.192 | 0.290 | 0.344 | 0.399 | 0.501 | 1.00 | 890 |
| gamma[13,3,2] | 0.333 | 0.069 | 0.198 | 0.288 | 0.332 | 0.379 | 0.471 | 1.00 | 2300 |
| gamma[14,3,2] | 0.055 | 0.020 | 0.018 | 0.041 | 0.054 | 0.068 | 0.098 | 1.03 | 160 |
| gamma[15,3,2] | 0.105 | 0.088 | 0.003 | 0.033 | 0.079 | 0.162 | 0.308 | 1.01 | 590 |
| gamma[16,3,2] | 0.006 | 0.005 | 0.000 | 0.003 | 0.005 | 0.008 | 0.018 | 1.01 | 350 |
| gamma[17,3,2] | 0.032 | 0.016 | 0.005 | 0.020 | 0.030 | 0.041 | 0.066 | 1.01 | 810 |
| gamma[18,3,2] | 0.048 | 0.018 | 0.020 | 0.036 | 0.047 | 0.058 | 0.090 | 1.01 | 180 |
| gamma[19,3,2] | 0.127 | 0.057 | 0.026 | 0.084 | 0.124 | 0.164 | 0.242 | 1.04 | 110 |
| gamma[20,3,2] | 0.064 | 0.037 | 0.004 | 0.035 | 0.061 | 0.088 | 0.146 | 1.03 | 150 |
| gamma[21,3,2] | 0.294 | 0.100 | 0.088 | 0.226 | 0.298 | 0.367 | 0.476 | 1.03 | 110 |
| gamma[22,3,2] | 0.262 | 0.059 | 0.154 | 0.220 | 0.260 | 0.301 | 0.380 | 1.01 | 490 |
| gamma[23,3,2] | 0.057 | 0.053 | 0.002 | 0.018 | 0.040 | 0.082 | 0.197 | 1.01 | 850 |
| gamma[24,3,2] | 0.272 | 0.070 | 0.134 | 0.225 | 0.271 | 0.319 | 0.406 | 1.00 | 680 |
| gamma[25,3,2] | 0.069 | 0.063 | 0.002 | 0.022 | 0.052 | 0.098 | 0.229 | 1.00 | 780 |
| gamma[1,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[2,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[3,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[4,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[5,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[6,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[7,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[8,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[9,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[10,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[11,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[12,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[13,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[14,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[15,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[16,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[17,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[18,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[19,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[20,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[21,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[22,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[23,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[24,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[25,1,3] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| gamma[1,2,3] | 0.038 | 0.046 | 0.000 | 0.005 | 0.022 | 0.056 | 0.156 | 1.01 | 570 |
| gamma[2,2,3] | 0.040 | 0.047 | 0.000 | 0.005 | 0.022 | 0.061 | 0.171 | 1.02 | 660 |
| gamma[3,2,3] | 0.299 | 0.117 | 0.062 | 0.216 | 0.305 | 0.385 | 0.519 | 1.01 | 290 |
| gamma[4,2,3] | 0.043 | 0.054 | 0.000 | 0.005 | 0.022 | 0.058 | 0.200 | 1.02 | 150 |
| gamma[5,2,3] | 0.069 | 0.074 | 0.000 | 0.012 | 0.044 | 0.103 | 0.262 | 1.10 | 63 |
| gamma[6,2,3] | 0.149 | 0.116 | 0.001 | 0.050 | 0.131 | 0.228 | 0.405 | 1.00 | 4000 |
| gamma[7,2,3] | 0.315 | 0.146 | 0.025 | 0.208 | 0.326 | 0.425 | 0.573 | 1.14 | 83 |
| gamma[8,2,3] | 0.048 | 0.053 | 0.000 | 0.008 | 0.030 | 0.069 | 0.193 | 1.00 | 550 |
| gamma[9,2,3] | 0.111 | 0.091 | 0.001 | 0.044 | 0.090 | 0.153 | 0.357 | 1.07 | 100 |
| gamma[10,2,3] | 0.027 | 0.035 | 0.000 | 0.002 | 0.013 | 0.039 | 0.128 | 1.02 | 220 |
| gamma[11,2,3] | 0.317 | 0.054 | 0.215 | 0.281 | 0.316 | 0.354 | 0.424 | 1.01 | 390 |
| gamma[12,2,3] | 0.023 | 0.027 | 0.000 | 0.003 | 0.013 | 0.035 | 0.090 | 1.05 | 100 |
| gamma[13,2,3] | 0.021 | 0.026 | 0.000 | 0.002 | 0.011 | 0.030 | 0.091 | 1.01 | 570 |
| gamma[14,2,3] | 0.729 | 0.053 | 0.616 | 0.696 | 0.732 | 0.766 | 0.827 | 1.00 | 1300 |
| gamma[15,2,3] | 0.370 | 0.159 | 0.035 | 0.267 | 0.383 | 0.491 | 0.642 | 1.04 | 360 |
| gamma[16,2,3] | 0.892 | 0.027 | 0.830 | 0.875 | 0.895 | 0.911 | 0.938 | 1.01 | 470 |
| gamma[17,2,3] | 0.623 | 0.083 | 0.448 | 0.570 | 0.628 | 0.682 | 0.771 | 1.01 | 480 |
| gamma[18,2,3] | 0.587 | 0.083 | 0.407 | 0.534 | 0.595 | 0.644 | 0.730 | 1.02 | 200 |
| gamma[19,2,3] | 0.015 | 0.019 | 0.000 | 0.002 | 0.008 | 0.020 | 0.065 | 1.01 | 310 |
| gamma[20,2,3] | 0.022 | 0.029 | 0.000 | 0.003 | 0.011 | 0.030 | 0.106 | 1.08 | 69 |
| gamma[21,2,3] | 0.014 | 0.020 | 0.000 | 0.001 | 0.007 | 0.019 | 0.071 | 1.01 | 310 |
| gamma[22,2,3] | 0.020 | 0.024 | 0.000 | 0.003 | 0.011 | 0.029 | 0.088 | 1.05 | 280 |
| gamma[23,2,3] | 0.187 | 0.046 | 0.104 | 0.157 | 0.186 | 0.217 | 0.283 | 1.01 | 210 |
| gamma[24,2,3] | 0.021 | 0.027 | 0.000 | 0.002 | 0.010 | 0.030 | 0.095 | 1.03 | 310 |
| gamma[25,2,3] | 0.368 | 0.058 | 0.258 | 0.328 | 0.366 | 0.406 | 0.487 | 1.00 | 1500 |
| gamma[1,3,3] | 0.949 | 0.023 | 0.898 | 0.934 | 0.952 | 0.966 | 0.988 | 1.00 | 1100 |
| gamma[2,3,3] | 0.965 | 0.019 | 0.922 | 0.953 | 0.968 | 0.980 | 0.996 | 1.00 | 1100 |
| gamma[3,3,3] | 0.967 | 0.019 | 0.921 | 0.956 | 0.970 | 0.982 | 0.995 | 1.00 | 650 |
| gamma[4,3,3] | 0.829 | 0.060 | 0.709 | 0.789 | 0.829 | 0.868 | 0.952 | 1.04 | 70 |
| gamma[5,3,3] | 0.882 | 0.042 | 0.793 | 0.855 | 0.885 | 0.912 | 0.956 | 1.02 | 160 |
| gamma[6,3,3] | 0.959 | 0.024 | 0.905 | 0.944 | 0.962 | 0.976 | 0.995 | 1.01 | 230 |
| gamma[7,3,3] | 0.976 | 0.014 | 0.943 | 0.967 | 0.978 | 0.986 | 0.997 | 1.01 | 430 |
| gamma[8,3,3] | 0.770 | 0.055 | 0.655 | 0.735 | 0.772 | 0.808 | 0.871 | 1.00 | 2300 |
| gamma[9,3,3] | 0.672 | 0.163 | 0.372 | 0.549 | 0.666 | 0.793 | 0.972 | 1.01 | 200 |
| gamma[10,3,3] | 0.933 | 0.058 | 0.789 | 0.901 | 0.949 | 0.978 | 0.999 | 1.00 | 1400 |
| gamma[11,3,3] | 0.935 | 0.059 | 0.784 | 0.909 | 0.951 | 0.979 | 0.997 | 1.01 | 340 |
| gamma[12,3,3] | 0.655 | 0.079 | 0.499 | 0.601 | 0.656 | 0.710 | 0.808 | 1.00 | 660 |
| gamma[13,3,3] | 0.667 | 0.069 | 0.529 | 0.621 | 0.668 | 0.712 | 0.802 | 1.00 | 2900 |
| gamma[14,3,3] | 0.945 | 0.020 | 0.902 | 0.932 | 0.946 | 0.959 | 0.982 | 1.01 | 220 |
| gamma[15,3,3] | 0.895 | 0.088 | 0.692 | 0.838 | 0.921 | 0.967 | 0.997 | 1.00 | 950 |
| gamma[16,3,3] | 0.994 | 0.005 | 0.982 | 0.992 | 0.995 | 0.997 | 1.000 | 1.00 | 630 |
| gamma[17,3,3] | 0.968 | 0.016 | 0.934 | 0.959 | 0.970 | 0.980 | 0.995 | 1.00 | 1100 |
| gamma[18,3,3] | 0.952 | 0.018 | 0.910 | 0.942 | 0.953 | 0.964 | 0.980 | 1.02 | 160 |
| gamma[19,3,3] | 0.873 | 0.057 | 0.758 | 0.836 | 0.876 | 0.916 | 0.974 | 1.02 | 100 |
| gamma[20,3,3] | 0.936 | 0.037 | 0.854 | 0.912 | 0.939 | 0.965 | 0.996 | 1.01 | 170 |
| gamma[21,3,3] | 0.706 | 0.100 | 0.524 | 0.633 | 0.702 | 0.774 | 0.912 | 1.03 | 87 |
| gamma[22,3,3] | 0.738 | 0.059 | 0.620 | 0.699 | 0.740 | 0.780 | 0.846 | 1.01 | 530 |
| gamma[23,3,3] | 0.943 | 0.053 | 0.803 | 0.918 | 0.960 | 0.982 | 0.998 | 1.01 | 1500 |
| gamma[24,3,3] | 0.728 | 0.070 | 0.594 | 0.681 | 0.729 | 0.775 | 0.866 | 1.00 | 590 |
| gamma[25,3,3] | 0.931 | 0.063 | 0.771 | 0.902 | 0.948 | 0.978 | 0.998 | 1.01 | 450 |
| inv.phi[1,1] | 3.368 | 1.621 | 0.951 | 2.208 | 3.123 | 4.248 | 7.213 | 1.09 | 35 |
| inv.phi[2,1] | -0.434 | 1.046 | -2.551 | -1.127 | -0.389 | 0.292 | 1.487 | 1.02 | 170 |
| inv.phi[3,1] | -1.271 | 1.144 | -3.926 | -1.901 | -1.155 | -0.480 | 0.566 | 1.01 | 220 |
| inv.phi[4,1] | -1.270 | 1.197 | -3.901 | -1.972 | -1.152 | -0.480 | 0.762 | 1.01 | 240 |
| inv.phi[1,2] | -0.434 | 1.046 | -2.551 | -1.127 | -0.389 | 0.292 | 1.487 | 1.02 | 170 |
| inv.phi[2,2] | 2.938 | 1.426 | 0.879 | 1.881 | 2.730 | 3.737 | 6.381 | 1.02 | 270 |
| inv.phi[3,2] | -0.254 | 1.008 | -2.250 | -0.892 | -0.282 | 0.371 | 1.783 | 1.02 | 180 |
| inv.phi[4,2] | -1.826 | 1.412 | -5.249 | -2.569 | -1.605 | -0.826 | 0.326 | 1.02 | 150 |
| inv.phi[1,3] | -1.271 | 1.144 | -3.926 | -1.901 | -1.155 | -0.480 | 0.566 | 1.01 | 220 |
| inv.phi[2,3] | -0.254 | 1.008 | -2.250 | -0.892 | -0.282 | 0.371 | 1.783 | 1.02 | 180 |
| inv.phi[3,3] | 2.641 | 1.417 | 0.638 | 1.597 | 2.371 | 3.383 | 6.162 | 1.02 | 260 |
| inv.phi[4,3] | -0.757 | 1.193 | -3.407 | -1.501 | -0.619 | 0.110 | 1.213 | 1.01 | 320 |
| inv.phi[1,4] | -1.270 | 1.197 | -3.901 | -1.972 | -1.152 | -0.480 | 0.762 | 1.01 | 240 |
| inv.phi[2,4] | -1.826 | 1.412 | -5.249 | -2.569 | -1.605 | -0.826 | 0.326 | 1.02 | 150 |
| inv.phi[3,4] | -0.757 | 1.193 | -3.407 | -1.501 | -0.619 | 0.110 | 1.213 | 1.01 | 320 |
| inv.phi[4,4] | 3.537 | 2.014 | 0.764 | 2.051 | 3.114 | 4.649 | 8.520 | 1.03 | 120 |
| lambda[1] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| lambda[2] | 1.367 | 0.226 | 1.000 | 1.197 | 1.335 | 1.524 | 1.827 | 1.03 | 110 |
| lambda[3] | 1.251 | 0.209 | 0.876 | 1.105 | 1.236 | 1.388 | 1.675 | 1.02 | 130 |
| lambda[4] | 1.152 | 0.224 | 0.736 | 1.000 | 1.145 | 1.304 | 1.612 | 1.06 | 50 |
| lambda[5] | 1.475 | 0.315 | 0.935 | 1.254 | 1.462 | 1.657 | 2.203 | 1.08 | 39 |
| lambda[6] | 1.205 | 0.244 | 0.812 | 1.023 | 1.171 | 1.367 | 1.731 | 1.09 | 35 |
| lambda[7] | 1.450 | 0.255 | 0.933 | 1.290 | 1.441 | 1.618 | 1.956 | 1.09 | 33 |
| lambda[8] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| lambda[9] | 0.708 | 0.094 | 0.536 | 0.645 | 0.705 | 0.768 | 0.908 | 1.01 | 180 |
| lambda[10] | 0.588 | 0.083 | 0.446 | 0.533 | 0.581 | 0.638 | 0.763 | 1.02 | 160 |
| lambda[11] | 0.614 | 0.083 | 0.452 | 0.557 | 0.613 | 0.668 | 0.779 | 1.01 | 1000 |
| lambda[12] | 0.940 | 0.141 | 0.686 | 0.838 | 0.933 | 1.034 | 1.238 | 1.03 | 78 |
| lambda[13] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| lambda[14] | 1.130 | 0.268 | 0.632 | 0.949 | 1.124 | 1.287 | 1.699 | 1.07 | 44 |
| lambda[15] | 0.439 | 0.096 | 0.298 | 0.373 | 0.422 | 0.486 | 0.671 | 1.01 | 310 |
| lambda[16] | 1.275 | 0.233 | 0.859 | 1.105 | 1.258 | 1.432 | 1.769 | 1.13 | 25 |
| lambda[17] | 1.241 | 0.243 | 0.793 | 1.065 | 1.228 | 1.405 | 1.729 | 1.08 | 39 |
| lambda[18] | 1.364 | 0.290 | 0.887 | 1.156 | 1.337 | 1.527 | 2.031 | 1.05 | 160 |
| lambda[19] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| lambda[20] | 1.029 | 0.151 | 0.757 | 0.927 | 1.020 | 1.120 | 1.365 | 1.07 | 43 |
| lambda[21] | 0.870 | 0.140 | 0.634 | 0.765 | 0.858 | 0.963 | 1.162 | 1.08 | 42 |
| lambda[22] | 1.120 | 0.142 | 0.868 | 1.021 | 1.110 | 1.205 | 1.430 | 1.02 | 180 |
| lambda[23] | 0.780 | 0.101 | 0.591 | 0.708 | 0.776 | 0.843 | 0.986 | 1.01 | 300 |
| lambda[24] | 0.867 | 0.118 | 0.657 | 0.787 | 0.859 | 0.935 | 1.131 | 1.02 | 140 |
| lambda[25] | 0.711 | 0.096 | 0.536 | 0.644 | 0.708 | 0.774 | 0.907 | 1.01 | 290 |
| lambda.std[1] | 0.707 | 0.000 | 0.707 | 0.707 | 0.707 | 0.707 | 0.707 | 1.00 | 1 |
| lambda.std[2] | 0.800 | 0.046 | 0.707 | 0.767 | 0.800 | 0.836 | 0.877 | 1.03 | 97 |
| lambda.std[3] | 0.773 | 0.052 | 0.659 | 0.742 | 0.777 | 0.811 | 0.859 | 1.02 | 120 |
| lambda.std[4] | 0.745 | 0.066 | 0.593 | 0.707 | 0.753 | 0.794 | 0.850 | 1.07 | 47 |
| lambda.std[5] | 0.816 | 0.058 | 0.683 | 0.782 | 0.825 | 0.856 | 0.911 | 1.09 | 38 |
| lambda.std[6] | 0.759 | 0.062 | 0.630 | 0.715 | 0.760 | 0.807 | 0.866 | 1.08 | 38 |
| lambda.std[7] | 0.815 | 0.051 | 0.682 | 0.790 | 0.822 | 0.851 | 0.890 | 1.08 | 39 |
| lambda.std[8] | 0.707 | 0.000 | 0.707 | 0.707 | 0.707 | 0.707 | 0.707 | 1.00 | 1 |
| lambda.std[9] | 0.574 | 0.051 | 0.473 | 0.542 | 0.576 | 0.609 | 0.672 | 1.01 | 190 |
| lambda.std[10] | 0.504 | 0.052 | 0.407 | 0.470 | 0.503 | 0.538 | 0.607 | 1.02 | 160 |
| lambda.std[11] | 0.520 | 0.051 | 0.412 | 0.487 | 0.522 | 0.556 | 0.615 | 1.01 | 880 |
| lambda.std[12] | 0.679 | 0.055 | 0.566 | 0.642 | 0.682 | 0.719 | 0.778 | 1.03 | 82 |
| lambda.std[13] | 0.707 | 0.000 | 0.707 | 0.707 | 0.707 | 0.707 | 0.707 | 1.00 | 1 |
| lambda.std[14] | 0.734 | 0.082 | 0.534 | 0.688 | 0.747 | 0.790 | 0.862 | 1.07 | 44 |
| lambda.std[15] | 0.398 | 0.070 | 0.285 | 0.349 | 0.389 | 0.437 | 0.557 | 1.01 | 290 |
| lambda.std[16] | 0.778 | 0.056 | 0.652 | 0.741 | 0.783 | 0.820 | 0.870 | 1.13 | 26 |
| lambda.std[17] | 0.768 | 0.064 | 0.621 | 0.729 | 0.775 | 0.815 | 0.866 | 1.06 | 48 |
| lambda.std[18] | 0.795 | 0.060 | 0.663 | 0.756 | 0.801 | 0.837 | 0.897 | 1.03 | 370 |
| lambda.std[19] | 0.707 | 0.000 | 0.707 | 0.707 | 0.707 | 0.707 | 0.707 | 1.00 | 1 |
| lambda.std[20] | 0.712 | 0.051 | 0.604 | 0.680 | 0.714 | 0.746 | 0.807 | 1.07 | 42 |
| lambda.std[21] | 0.650 | 0.059 | 0.536 | 0.608 | 0.651 | 0.694 | 0.758 | 1.07 | 46 |
| lambda.std[22] | 0.742 | 0.042 | 0.655 | 0.714 | 0.743 | 0.770 | 0.820 | 1.02 | 180 |
| lambda.std[23] | 0.611 | 0.049 | 0.509 | 0.578 | 0.613 | 0.645 | 0.702 | 1.01 | 310 |
| lambda.std[24] | 0.651 | 0.050 | 0.549 | 0.619 | 0.652 | 0.683 | 0.749 | 1.02 | 150 |
| lambda.std[25] | 0.576 | 0.052 | 0.473 | 0.542 | 0.578 | 0.612 | 0.672 | 1.01 | 290 |
| phi[1,1] | 3.603 | 2.298 | 1.010 | 2.176 | 3.039 | 4.428 | 9.327 | 1.08 | 49 |
| phi[2,1] | 3.059 | 1.887 | 0.754 | 1.859 | 2.687 | 3.751 | 7.649 | 1.06 | 58 |
| phi[3,1] | 3.117 | 2.247 | 0.498 | 1.740 | 2.621 | 3.797 | 9.606 | 1.11 | 52 |
| phi[4,1] | 3.399 | 1.320 | 1.151 | 2.559 | 3.257 | 4.097 | 6.377 | 1.10 | 49 |
| phi[1,2] | 3.059 | 1.887 | 0.754 | 1.859 | 2.687 | 3.751 | 7.649 | 1.06 | 58 |
| phi[2,2] | 4.064 | 2.455 | 1.060 | 2.296 | 3.487 | 5.147 | 10.603 | 1.04 | 67 |
| phi[3,2] | 3.074 | 2.152 | 0.374 | 1.607 | 2.594 | 4.017 | 9.025 | 1.13 | 35 |
| phi[4,2] | 3.673 | 1.507 | 1.041 | 2.686 | 3.529 | 4.491 | 7.169 | 1.06 | 50 |
| phi[1,3] | 3.117 | 2.247 | 0.498 | 1.740 | 2.621 | 3.797 | 9.606 | 1.11 | 52 |
| phi[2,3] | 3.074 | 2.152 | 0.374 | 1.607 | 2.594 | 4.017 | 9.025 | 1.13 | 35 |
| phi[3,3] | 4.037 | 2.948 | 0.836 | 2.032 | 3.281 | 4.972 | 12.539 | 1.09 | 39 |
| phi[4,3] | 3.327 | 1.665 | 0.208 | 2.156 | 3.261 | 4.303 | 7.106 | 1.10 | 37 |
| phi[1,4] | 3.399 | 1.320 | 1.151 | 2.559 | 3.257 | 4.097 | 6.377 | 1.10 | 49 |
| phi[2,4] | 3.673 | 1.507 | 1.041 | 2.686 | 3.529 | 4.491 | 7.169 | 1.06 | 50 |
| phi[3,4] | 3.327 | 1.665 | 0.208 | 2.156 | 3.261 | 4.303 | 7.106 | 1.10 | 37 |
| phi[4,4] | 4.476 | 0.675 | 3.437 | 4.023 | 4.361 | 4.815 | 6.150 | 1.06 | 87 |
| phi.cor[1,1] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| phi.cor[2,1] | 0.815 | 0.135 | 0.441 | 0.759 | 0.857 | 0.910 | 0.964 | 1.04 | 140 |
| phi.cor[3,1] | 0.821 | 0.178 | 0.331 | 0.782 | 0.877 | 0.928 | 0.975 | 1.08 | 110 |
| phi.cor[4,1] | 0.864 | 0.126 | 0.470 | 0.842 | 0.908 | 0.938 | 0.970 | 1.06 | 84 |
| phi.cor[1,2] | 0.815 | 0.135 | 0.441 | 0.759 | 0.857 | 0.910 | 0.964 | 1.04 | 140 |
| phi.cor[2,2] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| phi.cor[3,2] | 0.769 | 0.195 | 0.216 | 0.715 | 0.834 | 0.896 | 0.967 | 1.06 | 94 |
| phi.cor[4,2] | 0.873 | 0.156 | 0.462 | 0.862 | 0.922 | 0.950 | 0.976 | 1.21 | 45 |
| phi.cor[1,3] | 0.821 | 0.178 | 0.331 | 0.782 | 0.877 | 0.928 | 0.975 | 1.08 | 110 |
| phi.cor[2,3] | 0.769 | 0.195 | 0.216 | 0.715 | 0.834 | 0.896 | 0.967 | 1.06 | 94 |
| phi.cor[3,3] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| phi.cor[4,3] | 0.795 | 0.218 | 0.100 | 0.751 | 0.880 | 0.931 | 0.968 | 1.07 | 73 |
| phi.cor[1,4] | 0.864 | 0.126 | 0.470 | 0.842 | 0.908 | 0.938 | 0.970 | 1.06 | 84 |
| phi.cor[2,4] | 0.873 | 0.156 | 0.462 | 0.862 | 0.922 | 0.950 | 0.976 | 1.21 | 45 |
| phi.cor[3,4] | 0.795 | 0.218 | 0.100 | 0.751 | 0.880 | 0.931 | 0.968 | 1.07 | 73 |
| phi.cor[4,4] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| reli.omega[1] | 0.917 | 0.016 | 0.882 | 0.909 | 0.918 | 0.927 | 0.943 | 1.12 | 28 |
| reli.omega[2] | 0.796 | 0.018 | 0.756 | 0.784 | 0.797 | 0.809 | 0.826 | 1.04 | 83 |
| reli.omega[3] | 0.872 | 0.021 | 0.827 | 0.857 | 0.873 | 0.888 | 0.906 | 1.07 | 47 |
| reli.omega[4] | 0.841 | 0.018 | 0.799 | 0.830 | 0.842 | 0.853 | 0.873 | 1.04 | 80 |
| tau[1,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[2,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[3,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[4,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[5,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[6,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[7,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[8,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[9,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[10,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[11,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[12,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[13,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[14,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[15,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[16,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[17,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[18,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[19,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[20,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[21,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[22,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[23,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[24,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[25,1] | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1.00 | 1 |
| tau[1,2] | 3.889 | 0.324 | 3.329 | 3.670 | 3.859 | 4.087 | 4.617 | 1.03 | 140 |
| tau[2,2] | 4.801 | 0.716 | 3.673 | 4.239 | 4.703 | 5.325 | 6.255 | 1.02 | 110 |
| tau[3,2] | 4.220 | 0.577 | 3.253 | 3.808 | 4.164 | 4.589 | 5.453 | 1.01 | 230 |
| tau[4,2] | 3.511 | 0.722 | 2.253 | 2.987 | 3.490 | 3.979 | 5.021 | 1.07 | 43 |
| tau[5,2] | 4.637 | 0.972 | 3.082 | 3.881 | 4.593 | 5.259 | 6.692 | 1.06 | 49 |
| tau[6,2] | 4.170 | 0.732 | 3.031 | 3.629 | 4.067 | 4.608 | 5.786 | 1.07 | 51 |
| tau[7,2] | 5.432 | 0.764 | 4.023 | 4.908 | 5.384 | 5.927 | 7.034 | 1.06 | 47 |
| tau[8,2] | 3.099 | 0.325 | 2.532 | 2.870 | 3.076 | 3.304 | 3.804 | 1.03 | 100 |
| tau[9,2] | 1.132 | 0.251 | 0.643 | 0.982 | 1.128 | 1.280 | 1.634 | 1.01 | 320 |
| tau[10,2] | 1.539 | 0.155 | 1.287 | 1.433 | 1.523 | 1.626 | 1.878 | 1.00 | 3600 |
| tau[11,2] | 0.056 | 0.046 | 0.002 | 0.019 | 0.045 | 0.081 | 0.171 | 1.00 | 4000 |
| tau[12,2] | 2.690 | 0.458 | 1.988 | 2.366 | 2.623 | 2.932 | 3.807 | 1.03 | 90 |
| tau[13,2] | 2.758 | 0.288 | 2.245 | 2.554 | 2.737 | 2.939 | 3.371 | 1.01 | 230 |
| tau[14,2] | 4.573 | 0.863 | 3.093 | 3.968 | 4.498 | 5.108 | 6.466 | 1.06 | 49 |
| tau[15,2] | 1.722 | 0.566 | 1.200 | 1.400 | 1.555 | 1.839 | 3.081 | 1.02 | 820 |
| tau[16,2] | 5.510 | 0.703 | 4.277 | 4.993 | 5.460 | 5.987 | 7.001 | 1.08 | 35 |
| tau[17,2] | 4.676 | 0.741 | 3.365 | 4.152 | 4.626 | 5.147 | 6.250 | 1.05 | 62 |
| tau[18,2] | 5.255 | 0.865 | 3.841 | 4.684 | 5.193 | 5.681 | 7.469 | 1.08 | 68 |
| tau[19,2] | 2.694 | 0.264 | 2.249 | 2.509 | 2.665 | 2.847 | 3.308 | 1.04 | 78 |
| tau[20,2] | 3.450 | 0.509 | 2.655 | 3.077 | 3.382 | 3.762 | 4.636 | 1.04 | 62 |
| tau[21,2] | 2.429 | 0.480 | 1.715 | 2.062 | 2.343 | 2.741 | 3.488 | 1.06 | 48 |
| tau[22,2] | 3.267 | 0.431 | 2.558 | 2.958 | 3.222 | 3.529 | 4.234 | 1.02 | 120 |
| tau[23,2] | 0.042 | 0.036 | 0.001 | 0.014 | 0.033 | 0.060 | 0.135 | 1.00 | 4000 |
| tau[24,2] | 2.854 | 0.411 | 2.184 | 2.562 | 2.810 | 3.087 | 3.812 | 1.01 | 270 |
| tau[25,2] | 0.033 | 0.030 | 0.001 | 0.011 | 0.025 | 0.048 | 0.109 | 1.00 | 3500 |
| theta[1] | 2.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 1.00 | 1 |
| theta[2] | 2.919 | 0.645 | 2.001 | 2.432 | 2.782 | 3.323 | 4.338 | 1.03 | 110 |
| theta[3] | 2.608 | 0.543 | 1.768 | 2.221 | 2.528 | 2.927 | 3.807 | 1.02 | 140 |
| theta[4] | 2.378 | 0.528 | 1.542 | 2.000 | 2.311 | 2.702 | 3.600 | 1.06 | 54 |
| theta[5] | 3.274 | 0.995 | 1.874 | 2.572 | 3.136 | 3.746 | 5.854 | 1.08 | 40 |
| theta[6] | 2.512 | 0.626 | 1.660 | 2.046 | 2.370 | 2.868 | 3.996 | 1.10 | 34 |
| theta[7] | 3.168 | 0.750 | 1.870 | 2.665 | 3.076 | 3.618 | 4.827 | 1.10 | 30 |
| theta[8] | 2.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 1.00 | 1 |
| theta[9] | 1.510 | 0.136 | 1.288 | 1.416 | 1.497 | 1.589 | 1.825 | 1.01 | 180 |
| theta[10] | 1.353 | 0.102 | 1.199 | 1.284 | 1.338 | 1.407 | 1.583 | 1.02 | 160 |
| theta[11] | 1.384 | 0.103 | 1.205 | 1.310 | 1.375 | 1.447 | 1.607 | 1.00 | 1600 |
| theta[12] | 1.903 | 0.273 | 1.470 | 1.702 | 1.871 | 2.069 | 2.533 | 1.04 | 74 |
| theta[13] | 2.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 1.00 | 1 |
| theta[14] | 2.350 | 0.641 | 1.399 | 1.900 | 2.264 | 2.655 | 3.887 | 1.06 | 45 |
| theta[15] | 1.202 | 0.095 | 1.089 | 1.139 | 1.178 | 1.236 | 1.451 | 1.01 | 410 |
| theta[16] | 2.681 | 0.618 | 1.738 | 2.220 | 2.583 | 3.051 | 4.128 | 1.13 | 25 |
| theta[17] | 2.598 | 0.619 | 1.629 | 2.133 | 2.507 | 2.974 | 3.989 | 1.09 | 34 |
| theta[18] | 2.944 | 0.852 | 1.786 | 2.336 | 2.787 | 3.331 | 5.127 | 1.06 | 110 |
| theta[19] | 2.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 1.00 | 1 |
| theta[20] | 2.082 | 0.321 | 1.574 | 1.859 | 2.039 | 2.255 | 2.862 | 1.06 | 44 |
| theta[21] | 1.776 | 0.252 | 1.402 | 1.585 | 1.736 | 1.928 | 2.350 | 1.08 | 39 |
| theta[22] | 2.274 | 0.327 | 1.753 | 2.042 | 2.233 | 2.452 | 3.045 | 1.02 | 190 |
| theta[23] | 1.618 | 0.160 | 1.349 | 1.502 | 1.602 | 1.711 | 1.973 | 1.01 | 280 |
| theta[24] | 1.765 | 0.211 | 1.431 | 1.620 | 1.738 | 1.874 | 2.278 | 1.02 | 130 |
| theta[25] | 1.515 | 0.139 | 1.288 | 1.415 | 1.501 | 1.599 | 1.823 | 1.01 | 310 |
Posterior Distribution Summary
# extract for plotting
jags.mcmc <- as.mcmc(model.fit)
a <- colnames(as.data.frame(jags.mcmc[[1]]))
fit.mcmc <- data.frame(as.matrix(jags.mcmc, chains = T, iters = T))
colnames(fit.mcmc) <- c("chain", "iter", a)
fit.mcmc.ggs <- ggmcmc::ggs(jags.mcmc) # for GRB plotFactor Loadings (\(\lambda\))
bayesplot::mcmc_areas(fit.mcmc, regex_pars = "lambda.std", prob = 0.8); ggsave("fig/pools_model2_lambda_dens.pdf")
Saving 7 x 5 in imagebayesplot::mcmc_acf(fit.mcmc, regex_pars = "lambda.std"); ggsave("fig/pools_model2_lambda_acf.pdf")Warning: Removed 336 rows containing missing values (geom_segment).Warning: Removed 21 row(s) containing missing values (geom_path).
Saving 7 x 5 in imageWarning: Removed 336 rows containing missing values (geom_segment).
Warning: Removed 21 row(s) containing missing values (geom_path).bayesplot::mcmc_trace(fit.mcmc, regex_pars = "lambda.std"); ggsave("fig/pools_model2_lambda_trace.pdf")
Saving 7 x 5 in imageggmcmc::ggs_grb(fit.mcmc.ggs, family = "lambda.std"); ggsave("fig/pools_model2_lambda_grb.pdf")Warning: Removed 50 row(s) containing missing values (geom_path).
Saving 7 x 5 in imageWarning: Removed 50 row(s) containing missing values (geom_path).Factor Correlations
bayesplot::mcmc_areas(
fit.mcmc,
pars = c('phi.cor[1,2]', 'phi.cor[1,3]', 'phi.cor[1,4]',
'phi.cor[2,3]', 'phi.cor[2,4]', 'phi.cor[3,4]'), prob = 0.8)
bayesplot::mcmc_acf(
fit.mcmc,
pars = c('phi.cor[1,2]', 'phi.cor[1,3]', 'phi.cor[1,4]',
'phi.cor[2,3]', 'phi.cor[2,4]', 'phi.cor[3,4]'))
bayesplot::mcmc_trace(
fit.mcmc,
pars = c('phi.cor[1,2]', 'phi.cor[1,3]', 'phi.cor[1,4]',
'phi.cor[2,3]', 'phi.cor[2,4]', 'phi.cor[3,4]'))
ggmcmc::ggs_grb(fit.mcmc.ggs, family = "phi.cor")Warning: Removed 50 row(s) containing missing values (geom_path).
# save factor correlations
use.vars <- c('phi.cor[1,2]', 'phi.cor[1,3]', 'phi.cor[1,4]',
'phi.cor[2,3]', 'phi.cor[2,4]', 'phi.cor[3,4]')
extracted_cor <- fit.mcmc[,use.vars]
write.csv(x=extracted_cor, file=paste0(getwd(),"/data/pools/extracted_cor_m2.csv"))Misclassification
use.vars <- c("gamma[1,1,1]", "gamma[1,1,2]", "gamma[1,1,3]",
"gamma[1,2,1]", "gamma[1,2,2]", "gamma[1,2,3]",
"gamma[1,3,1]", "gamma[1,3,2]", "gamma[1,3,3]")
bayesplot::mcmc_areas(fit.mcmc, pars = use.vars, prob = 0.8); ggsave("fig/pools_model3_gamma_dens.pdf")
Saving 7 x 5 in imagebayesplot::mcmc_acf(fit.mcmc, pars = use.vars); ggsave("fig/pools_model3_gamma_acf.pdf")Warning: Removed 168 rows containing missing values (geom_segment).
Saving 7 x 5 in imageWarning: Removed 168 rows containing missing values (geom_segment).bayesplot::mcmc_trace(fit.mcmc, pars = use.vars); ggsave("fig/pools_model3_gamma_trace.pdf")
Saving 7 x 5 in imageFactor Reliability Omega (\(\omega\))
bayesplot::mcmc_areas(fit.mcmc, regex_pars = "reli.omega", prob = 0.8); ggsave("fig/pools_model2_omega_dens.pdf")
Saving 7 x 5 in imagebayesplot::mcmc_acf(fit.mcmc, regex_pars = "reli.omega"); ggsave("fig/pools_model2_omega_acf.pdf")
Saving 7 x 5 in imagebayesplot::mcmc_trace(fit.mcmc, regex_pars = "reli.omega"); ggsave("fig/pools_model2_omega_trace.pdf")
Saving 7 x 5 in imageggmcmc::ggs_grb(fit.mcmc.ggs, family = "reli.omega"); ggsave("fig/pools_model2_omega_grb.pdf")
Saving 7 x 5 in image# extract omega posterior for results comparison
extracted_omega <- data.frame(model_2_f1 = fit.mcmc$`reli.omega[1]`,
model_2_f2 = fit.mcmc$`reli.omega[2]`,
model_2_f3 = fit.mcmc$`reli.omega[3]`,
model_2_f4 = fit.mcmc$`reli.omega[4]`)
write.csv(x=extracted_omega, file=paste0(getwd(),"/data/pools/extracted_omega_m2.csv"))Relationship between factor loading and misclassification
keep.var <- c(
paste0('lambda.std[',1:25,']'),
paste0('gamma[',1:25,',1,1]'),
paste0('gamma[',1:25,',2,2]'),
paste0('gamma[',1:25,',3,3]')
)
#plot.dat <- fit.mcmc[,keep.var]
plot.dat <- data.frame(
item = c(paste0("Q4_",c(3:5,9,11,15,18)),
paste0("Q5_",c(1:3,5:6,12)),
paste0("Q6_",c(2,5:8, 11)),
paste0("Q7_",c(2, 4:5, 7:8, 14))),
factor = c(rep('EfL',7), rep('SC',6), rep('IN',6), rep('EnL',6)),
lambda.std = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('lambda.std[',1:25,']'),1],
`gamma[1,1]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',1,1]'),1],
`gamma[2,2]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',2,2]'),1],
`gamma[3,3]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',3,3]'),1],
`gamma[1,2]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',1,2]'),1],
`gamma[2,1]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',2,1]'),1],
`gamma[3,1]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',3,1]'),1],
`gamma[1,3]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',1,3]'),1],
`gamma[2,3]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',2,3]'),1],
`gamma[3,2]` = model.fit$BUGSoutput$summary[rownames(model.fit$BUGSoutput$summary)%in% paste0('gamma[',1:25,',3,2]'),1]
) %>%
pivot_longer(
cols = contains('gamma'),
names_to = 'gamma',
values_to = 'gamma_est'
)
ggplot(plot.dat, aes(x=gamma_est, y=lambda.std, color=factor))+
geom_text(aes(label = item)) +
facet_wrap(.~gamma, ncol=3)+
theme_bw()+
theme(
panel.grid = element_blank()
)
Manuscript Table and Figures
Table
# print to xtable
print(
xtable(
model.fit$BUGSoutput$summary,
caption = c("pools Model 1 posterior distribution summary")
,align = "lrrrrrrrrr"
),
include.rownames=T,
booktabs=T
)% latex table generated in R 4.0.5 by xtable 1.8-4 package
% Wed Feb 02 04:15:37 2022
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrrrrrr}
\toprule
& mean & sd & 2.5\% & 25\% & 50\% & 75\% & 97.5\% & Rhat & n.eff \\
\midrule
deviance & 16276.87 & 121.95 & 16038.85 & 16194.30 & 16276.70 & 16358.33 & 16513.89 & 1.01 & 500.00 \\
gamma[1,1,1] & 0.77 & 0.06 & 0.64 & 0.73 & 0.77 & 0.81 & 0.88 & 1.00 & 1500.00 \\
gamma[2,1,1] & 0.84 & 0.05 & 0.73 & 0.81 & 0.84 & 0.88 & 0.93 & 1.01 & 390.00 \\
gamma[3,1,1] & 0.61 & 0.09 & 0.44 & 0.55 & 0.62 & 0.67 & 0.77 & 1.01 & 310.00 \\
gamma[4,1,1] & 0.52 & 0.09 & 0.35 & 0.46 & 0.52 & 0.58 & 0.69 & 1.02 & 140.00 \\
gamma[5,1,1] & 0.70 & 0.07 & 0.55 & 0.65 & 0.70 & 0.75 & 0.83 & 1.03 & 110.00 \\
gamma[6,1,1] & 0.72 & 0.08 & 0.57 & 0.67 & 0.72 & 0.77 & 0.87 & 1.01 & 210.00 \\
gamma[7,1,1] & 0.77 & 0.07 & 0.61 & 0.72 & 0.77 & 0.82 & 0.90 & 1.02 & 110.00 \\
gamma[8,1,1] & 0.70 & 0.08 & 0.53 & 0.65 & 0.70 & 0.75 & 0.84 & 1.00 & 4000.00 \\
gamma[9,1,1] & 0.89 & 0.10 & 0.61 & 0.85 & 0.93 & 0.97 & 1.00 & 1.00 & 1700.00 \\
gamma[10,1,1] & 0.81 & 0.12 & 0.53 & 0.72 & 0.82 & 0.90 & 0.99 & 1.00 & 1500.00 \\
gamma[11,1,1] & 0.26 & 0.07 & 0.15 & 0.21 & 0.26 & 0.31 & 0.41 & 1.01 & 190.00 \\
gamma[12,1,1] & 0.96 & 0.03 & 0.88 & 0.94 & 0.97 & 0.98 & 1.00 & 1.01 & 380.00 \\
gamma[13,1,1] & 0.96 & 0.03 & 0.88 & 0.95 & 0.97 & 0.99 & 1.00 & 1.00 & 770.00 \\
gamma[14,1,1] & 0.57 & 0.11 & 0.36 & 0.50 & 0.57 & 0.65 & 0.79 & 1.00 & 890.00 \\
gamma[15,1,1] & 0.82 & 0.13 & 0.54 & 0.73 & 0.85 & 0.93 & 0.99 & 1.01 & 460.00 \\
gamma[16,1,1] & 0.63 & 0.10 & 0.42 & 0.57 & 0.64 & 0.70 & 0.82 & 1.00 & 820.00 \\
gamma[17,1,1] & 0.69 & 0.10 & 0.49 & 0.63 & 0.70 & 0.76 & 0.87 & 1.01 & 320.00 \\
gamma[18,1,1] & 0.59 & 0.11 & 0.37 & 0.52 & 0.60 & 0.67 & 0.79 & 1.03 & 150.00 \\
gamma[19,1,1] & 0.95 & 0.03 & 0.87 & 0.93 & 0.96 & 0.98 & 1.00 & 1.00 & 2700.00 \\
gamma[20,1,1] & 0.91 & 0.05 & 0.81 & 0.88 & 0.91 & 0.94 & 0.99 & 1.02 & 130.00 \\
gamma[21,1,1] & 0.87 & 0.07 & 0.71 & 0.82 & 0.88 & 0.93 & 0.99 & 1.02 & 130.00 \\
gamma[22,1,1] & 0.98 & 0.02 & 0.92 & 0.96 & 0.98 & 0.99 & 1.00 & 1.00 & 1400.00 \\
gamma[23,1,1] & 0.19 & 0.05 & 0.11 & 0.15 & 0.18 & 0.22 & 0.31 & 1.01 & 610.00 \\
gamma[24,1,1] & 0.96 & 0.04 & 0.86 & 0.94 & 0.96 & 0.98 & 1.00 & 1.00 & 990.00 \\
gamma[25,1,1] & 0.15 & 0.04 & 0.08 & 0.12 & 0.15 & 0.18 & 0.25 & 1.01 & 340.00 \\
gamma[1,2,1] & 0.02 & 0.02 & 0.00 & 0.00 & 0.01 & 0.02 & 0.06 & 1.00 & 3700.00 \\
gamma[2,2,1] & 0.01 & 0.01 & 0.00 & 0.00 & 0.00 & 0.01 & 0.04 & 1.02 & 290.00 \\
gamma[3,2,1] & 0.01 & 0.01 & 0.00 & 0.00 & 0.00 & 0.01 & 0.02 & 1.03 & 240.00 \\
gamma[4,2,1] & 0.01 & 0.01 & 0.00 & 0.00 & 0.00 & 0.01 & 0.03 & 1.00 & 670.00 \\
gamma[5,2,1] & 0.01 & 0.01 & 0.00 & 0.00 & 0.01 & 0.02 & 0.05 & 1.03 & 400.00 \\
gamma[6,2,1] & 0.02 & 0.02 & 0.00 & 0.01 & 0.02 & 0.03 & 0.07 & 1.06 & 89.00 \\
gamma[7,2,1] & 0.01 & 0.01 & 0.00 & 0.00 & 0.00 & 0.01 & 0.03 & 1.04 & 130.00 \\
gamma[8,2,1] & 0.02 & 0.02 & 0.00 & 0.00 & 0.01 & 0.03 & 0.07 & 1.10 & 52.00 \\
gamma[9,2,1] & 0.02 & 0.02 & 0.00 & 0.00 & 0.01 & 0.03 & 0.09 & 1.01 & 960.00 \\
gamma[10,2,1] & 0.02 & 0.02 & 0.00 & 0.00 & 0.01 & 0.03 & 0.09 & 1.01 & 650.00 \\
gamma[11,2,1] & 0.04 & 0.03 & 0.00 & 0.01 & 0.03 & 0.05 & 0.11 & 1.22 & 44.00 \\
gamma[12,2,1] & 0.08 & 0.06 & 0.00 & 0.03 & 0.07 & 0.12 & 0.20 & 1.04 & 120.00 \\
gamma[13,2,1] & 0.05 & 0.04 & 0.00 & 0.02 & 0.04 & 0.07 & 0.14 & 1.05 & 110.00 \\
gamma[14,2,1] & 0.01 & 0.01 & 0.00 & 0.00 & 0.01 & 0.01 & 0.03 & 1.09 & 48.00 \\
gamma[15,2,1] & 0.01 & 0.01 & 0.00 & 0.00 & 0.00 & 0.01 & 0.04 & 1.01 & 480.00 \\
gamma[16,2,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.01 & 1.01 & 310.00 \\
gamma[17,2,1] & 0.00 & 0.01 & 0.00 & 0.00 & 0.00 & 0.01 & 0.02 & 1.17 & 36.00 \\
gamma[18,2,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.02 & 1.04 & 87.00 \\
gamma[19,2,1] & 0.01 & 0.02 & 0.00 & 0.00 & 0.01 & 0.02 & 0.07 & 1.01 & 390.00 \\
gamma[20,2,1] & 0.02 & 0.02 & 0.00 & 0.00 & 0.01 & 0.03 & 0.08 & 1.02 & 950.00 \\
gamma[21,2,1] & 0.02 & 0.03 & 0.00 & 0.00 & 0.01 & 0.03 & 0.10 & 1.02 & 280.00 \\
gamma[22,2,1] & 0.04 & 0.04 & 0.00 & 0.01 & 0.03 & 0.06 & 0.12 & 1.01 & 630.00 \\
gamma[23,2,1] & 0.07 & 0.04 & 0.02 & 0.05 & 0.07 & 0.10 & 0.17 & 1.01 & 240.00 \\
gamma[24,2,1] & 0.03 & 0.04 & 0.00 & 0.01 & 0.02 & 0.05 & 0.13 & 1.04 & 110.00 \\
gamma[25,2,1] & 0.04 & 0.03 & 0.00 & 0.02 & 0.03 & 0.05 & 0.11 & 1.08 & 70.00 \\
gamma[1,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[2,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[3,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[4,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[5,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[6,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[7,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[8,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[9,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[10,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[11,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[12,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[13,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[14,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[15,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[16,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[17,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[18,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[19,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[20,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[21,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[22,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[23,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[24,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[25,3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[1,1,2] & 0.23 & 0.06 & 0.12 & 0.19 & 0.23 & 0.27 & 0.36 & 1.00 & 1300.00 \\
gamma[2,1,2] & 0.16 & 0.05 & 0.07 & 0.12 & 0.16 & 0.19 & 0.27 & 1.01 & 380.00 \\
gamma[3,1,2] & 0.39 & 0.09 & 0.23 & 0.33 & 0.38 & 0.45 & 0.56 & 1.01 & 300.00 \\
gamma[4,1,2] & 0.48 & 0.09 & 0.31 & 0.42 & 0.48 & 0.54 & 0.65 & 1.03 & 110.00 \\
gamma[5,1,2] & 0.30 & 0.07 & 0.17 & 0.25 & 0.30 & 0.35 & 0.45 & 1.02 & 110.00 \\
gamma[6,1,2] & 0.28 & 0.08 & 0.13 & 0.23 & 0.28 & 0.33 & 0.43 & 1.02 & 180.00 \\
gamma[7,1,2] & 0.23 & 0.07 & 0.10 & 0.18 & 0.23 & 0.28 & 0.39 & 1.02 & 110.00 \\
gamma[8,1,2] & 0.30 & 0.08 & 0.16 & 0.25 & 0.30 & 0.35 & 0.47 & 1.00 & 4000.00 \\
gamma[9,1,2] & 0.11 & 0.10 & 0.00 & 0.03 & 0.07 & 0.15 & 0.39 & 1.01 & 1100.00 \\
gamma[10,1,2] & 0.19 & 0.12 & 0.01 & 0.10 & 0.18 & 0.28 & 0.47 & 1.01 & 1100.00 \\
gamma[11,1,2] & 0.74 & 0.07 & 0.59 & 0.69 & 0.74 & 0.79 & 0.85 & 1.02 & 160.00 \\
gamma[12,1,2] & 0.04 & 0.03 & 0.00 & 0.02 & 0.03 & 0.06 & 0.12 & 1.01 & 260.00 \\
gamma[13,1,2] & 0.04 & 0.03 & 0.00 & 0.01 & 0.03 & 0.05 & 0.12 & 1.00 & 800.00 \\
gamma[14,1,2] & 0.43 & 0.11 & 0.21 & 0.35 & 0.43 & 0.50 & 0.64 & 1.01 & 460.00 \\
gamma[15,1,2] & 0.18 & 0.13 & 0.01 & 0.07 & 0.15 & 0.27 & 0.46 & 1.01 & 290.00 \\
gamma[16,1,2] & 0.37 & 0.10 & 0.18 & 0.30 & 0.36 & 0.43 & 0.58 & 1.00 & 700.00 \\
gamma[17,1,2] & 0.31 & 0.10 & 0.13 & 0.24 & 0.30 & 0.37 & 0.51 & 1.01 & 490.00 \\
gamma[18,1,2] & 0.41 & 0.11 & 0.21 & 0.33 & 0.40 & 0.48 & 0.63 & 1.02 & 200.00 \\
gamma[19,1,2] & 0.05 & 0.03 & 0.00 & 0.02 & 0.04 & 0.07 & 0.13 & 1.01 & 1100.00 \\
gamma[20,1,2] & 0.09 & 0.05 & 0.01 & 0.06 & 0.09 & 0.12 & 0.19 & 1.08 & 90.00 \\
gamma[21,1,2] & 0.13 & 0.07 & 0.01 & 0.07 & 0.12 & 0.18 & 0.29 & 1.02 & 260.00 \\
gamma[22,1,2] & 0.02 & 0.02 & 0.00 & 0.01 & 0.02 & 0.04 & 0.08 & 1.00 & 980.00 \\
gamma[23,1,2] & 0.81 & 0.05 & 0.69 & 0.78 & 0.82 & 0.85 & 0.89 & 1.01 & 390.00 \\
gamma[24,1,2] & 0.04 & 0.04 & 0.00 & 0.02 & 0.04 & 0.06 & 0.14 & 1.01 & 470.00 \\
gamma[25,1,2] & 0.85 & 0.04 & 0.75 & 0.82 & 0.85 & 0.88 & 0.92 & 1.01 & 340.00 \\
gamma[1,2,2] & 0.95 & 0.05 & 0.82 & 0.92 & 0.96 & 0.98 & 1.00 & 1.00 & 4000.00 \\
gamma[2,2,2] & 0.95 & 0.05 & 0.82 & 0.93 & 0.97 & 0.99 & 1.00 & 1.01 & 1300.00 \\
gamma[3,2,2] & 0.70 & 0.12 & 0.48 & 0.61 & 0.69 & 0.78 & 0.93 & 1.02 & 170.00 \\
gamma[4,2,2] & 0.95 & 0.06 & 0.79 & 0.94 & 0.97 & 0.99 & 1.00 & 1.02 & 150.00 \\
gamma[5,2,2] & 0.92 & 0.08 & 0.71 & 0.88 & 0.94 & 0.97 & 1.00 & 1.02 & 180.00 \\
gamma[6,2,2] & 0.83 & 0.12 & 0.57 & 0.75 & 0.84 & 0.93 & 0.99 & 1.00 & 3500.00 \\
gamma[7,2,2] & 0.68 & 0.15 & 0.42 & 0.57 & 0.67 & 0.79 & 0.97 & 1.02 & 170.00 \\
gamma[8,2,2] & 0.93 & 0.06 & 0.78 & 0.91 & 0.95 & 0.98 & 1.00 & 1.01 & 280.00 \\
gamma[9,2,2] & 0.87 & 0.10 & 0.61 & 0.83 & 0.89 & 0.94 & 0.99 & 1.02 & 270.00 \\
gamma[10,2,2] & 0.95 & 0.04 & 0.84 & 0.93 & 0.96 & 0.98 & 1.00 & 1.00 & 2800.00 \\
gamma[11,2,2] & 0.65 & 0.05 & 0.55 & 0.61 & 0.65 & 0.68 & 0.75 & 1.01 & 330.00 \\
gamma[12,2,2] & 0.90 & 0.06 & 0.76 & 0.86 & 0.91 & 0.95 & 0.99 & 1.01 & 520.00 \\
gamma[13,2,2] & 0.93 & 0.05 & 0.82 & 0.90 & 0.94 & 0.96 & 0.99 & 1.01 & 450.00 \\
gamma[14,2,2] & 0.26 & 0.05 & 0.16 & 0.22 & 0.26 & 0.30 & 0.38 & 1.00 & 1100.00 \\
gamma[15,2,2] & 0.62 & 0.16 & 0.35 & 0.50 & 0.61 & 0.73 & 0.96 & 1.00 & 1900.00 \\
gamma[16,2,2] & 0.11 & 0.03 & 0.06 & 0.09 & 0.10 & 0.12 & 0.17 & 1.01 & 390.00 \\
gamma[17,2,2] & 0.37 & 0.08 & 0.22 & 0.31 & 0.37 & 0.43 & 0.55 & 1.01 & 460.00 \\
gamma[18,2,2] & 0.41 & 0.08 & 0.27 & 0.35 & 0.40 & 0.46 & 0.59 & 1.02 & 180.00 \\
gamma[19,2,2] & 0.97 & 0.03 & 0.90 & 0.96 & 0.98 & 0.99 & 1.00 & 1.02 & 370.00 \\
gamma[20,2,2] & 0.96 & 0.04 & 0.87 & 0.94 & 0.97 & 0.99 & 1.00 & 1.01 & 480.00 \\
gamma[21,2,2] & 0.96 & 0.03 & 0.87 & 0.95 & 0.97 & 0.99 & 1.00 & 1.01 & 630.00 \\
gamma[22,2,2] & 0.94 & 0.04 & 0.83 & 0.92 & 0.95 & 0.98 & 1.00 & 1.02 & 260.00 \\
gamma[23,2,2] & 0.74 & 0.04 & 0.65 & 0.71 & 0.74 & 0.77 & 0.82 & 1.00 & 1800.00 \\
gamma[24,2,2] & 0.95 & 0.05 & 0.82 & 0.92 & 0.96 & 0.98 & 1.00 & 1.01 & 420.00 \\
gamma[25,2,2] & 0.59 & 0.05 & 0.49 & 0.56 & 0.60 & 0.63 & 0.69 & 1.00 & 740.00 \\
gamma[1,3,2] & 0.05 & 0.02 & 0.01 & 0.03 & 0.05 & 0.07 & 0.10 & 1.00 & 860.00 \\
gamma[2,3,2] & 0.03 & 0.02 & 0.00 & 0.02 & 0.03 & 0.05 & 0.08 & 1.01 & 2900.00 \\
gamma[3,3,2] & 0.03 & 0.02 & 0.00 & 0.02 & 0.03 & 0.04 & 0.08 & 1.01 & 410.00 \\
gamma[4,3,2] & 0.17 & 0.06 & 0.05 & 0.13 & 0.17 & 0.21 & 0.29 & 1.08 & 57.00 \\
gamma[5,3,2] & 0.12 & 0.04 & 0.04 & 0.09 & 0.12 & 0.15 & 0.21 & 1.02 & 140.00 \\
gamma[6,3,2] & 0.04 & 0.02 & 0.00 & 0.02 & 0.04 & 0.06 & 0.10 & 1.03 & 240.00 \\
gamma[7,3,2] & 0.02 & 0.01 & 0.00 & 0.01 & 0.02 & 0.03 & 0.06 & 1.02 & 260.00 \\
gamma[8,3,2] & 0.23 & 0.06 & 0.13 & 0.19 & 0.23 & 0.27 & 0.34 & 1.00 & 1700.00 \\
gamma[9,3,2] & 0.33 & 0.16 & 0.03 & 0.21 & 0.33 & 0.45 & 0.63 & 1.01 & 370.00 \\
gamma[10,3,2] & 0.07 & 0.06 & 0.00 & 0.02 & 0.05 & 0.10 & 0.21 & 1.00 & 1700.00 \\
gamma[11,3,2] & 0.07 & 0.06 & 0.00 & 0.02 & 0.05 & 0.09 & 0.22 & 1.01 & 340.00 \\
gamma[12,3,2] & 0.35 & 0.08 & 0.19 & 0.29 & 0.34 & 0.40 & 0.50 & 1.00 & 890.00 \\
gamma[13,3,2] & 0.33 & 0.07 & 0.20 & 0.29 & 0.33 & 0.38 & 0.47 & 1.00 & 2300.00 \\
gamma[14,3,2] & 0.06 & 0.02 & 0.02 & 0.04 & 0.05 & 0.07 & 0.10 & 1.03 & 160.00 \\
gamma[15,3,2] & 0.10 & 0.09 & 0.00 & 0.03 & 0.08 & 0.16 & 0.31 & 1.01 & 590.00 \\
gamma[16,3,2] & 0.01 & 0.00 & 0.00 & 0.00 & 0.00 & 0.01 & 0.02 & 1.01 & 350.00 \\
gamma[17,3,2] & 0.03 & 0.02 & 0.01 & 0.02 & 0.03 & 0.04 & 0.07 & 1.01 & 810.00 \\
gamma[18,3,2] & 0.05 & 0.02 & 0.02 & 0.04 & 0.05 & 0.06 & 0.09 & 1.01 & 180.00 \\
gamma[19,3,2] & 0.13 & 0.06 & 0.03 & 0.08 & 0.12 & 0.16 & 0.24 & 1.04 & 110.00 \\
gamma[20,3,2] & 0.06 & 0.04 & 0.00 & 0.03 & 0.06 & 0.09 & 0.15 & 1.03 & 150.00 \\
gamma[21,3,2] & 0.29 & 0.10 & 0.09 & 0.23 & 0.30 & 0.37 & 0.48 & 1.03 & 110.00 \\
gamma[22,3,2] & 0.26 & 0.06 & 0.15 & 0.22 & 0.26 & 0.30 & 0.38 & 1.01 & 490.00 \\
gamma[23,3,2] & 0.06 & 0.05 & 0.00 & 0.02 & 0.04 & 0.08 & 0.20 & 1.01 & 850.00 \\
gamma[24,3,2] & 0.27 & 0.07 & 0.13 & 0.23 & 0.27 & 0.32 & 0.41 & 1.00 & 680.00 \\
gamma[25,3,2] & 0.07 & 0.06 & 0.00 & 0.02 & 0.05 & 0.10 & 0.23 & 1.00 & 780.00 \\
gamma[1,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[2,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[3,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[4,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[5,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[6,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[7,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[8,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[9,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[10,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[11,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[12,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[13,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[14,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[15,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[16,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[17,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[18,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[19,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[20,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[21,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[22,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[23,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[24,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[25,1,3] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
gamma[1,2,3] & 0.04 & 0.05 & 0.00 & 0.01 & 0.02 & 0.06 & 0.16 & 1.01 & 570.00 \\
gamma[2,2,3] & 0.04 & 0.05 & 0.00 & 0.00 & 0.02 & 0.06 & 0.17 & 1.02 & 660.00 \\
gamma[3,2,3] & 0.30 & 0.12 & 0.06 & 0.22 & 0.31 & 0.38 & 0.52 & 1.01 & 290.00 \\
gamma[4,2,3] & 0.04 & 0.05 & 0.00 & 0.01 & 0.02 & 0.06 & 0.20 & 1.02 & 150.00 \\
gamma[5,2,3] & 0.07 & 0.07 & 0.00 & 0.01 & 0.04 & 0.10 & 0.26 & 1.10 & 63.00 \\
gamma[6,2,3] & 0.15 & 0.12 & 0.00 & 0.05 & 0.13 & 0.23 & 0.40 & 1.00 & 4000.00 \\
gamma[7,2,3] & 0.31 & 0.15 & 0.02 & 0.21 & 0.33 & 0.43 & 0.57 & 1.14 & 83.00 \\
gamma[8,2,3] & 0.05 & 0.05 & 0.00 & 0.01 & 0.03 & 0.07 & 0.19 & 1.01 & 550.00 \\
gamma[9,2,3] & 0.11 & 0.09 & 0.00 & 0.04 & 0.09 & 0.15 & 0.36 & 1.07 & 100.00 \\
gamma[10,2,3] & 0.03 & 0.04 & 0.00 & 0.00 & 0.01 & 0.04 & 0.13 & 1.02 & 220.00 \\
gamma[11,2,3] & 0.32 & 0.05 & 0.22 & 0.28 & 0.32 & 0.35 & 0.42 & 1.01 & 390.00 \\
gamma[12,2,3] & 0.02 & 0.03 & 0.00 & 0.00 & 0.01 & 0.03 & 0.09 & 1.05 & 100.00 \\
gamma[13,2,3] & 0.02 & 0.03 & 0.00 & 0.00 & 0.01 & 0.03 & 0.09 & 1.01 & 570.00 \\
gamma[14,2,3] & 0.73 & 0.05 & 0.62 & 0.70 & 0.73 & 0.77 & 0.83 & 1.00 & 1300.00 \\
gamma[15,2,3] & 0.37 & 0.16 & 0.03 & 0.27 & 0.38 & 0.49 & 0.64 & 1.04 & 360.00 \\
gamma[16,2,3] & 0.89 & 0.03 & 0.83 & 0.88 & 0.89 & 0.91 & 0.94 & 1.01 & 470.00 \\
gamma[17,2,3] & 0.62 & 0.08 & 0.45 & 0.57 & 0.63 & 0.68 & 0.77 & 1.01 & 480.00 \\
gamma[18,2,3] & 0.59 & 0.08 & 0.41 & 0.53 & 0.59 & 0.64 & 0.73 & 1.02 & 200.00 \\
gamma[19,2,3] & 0.01 & 0.02 & 0.00 & 0.00 & 0.01 & 0.02 & 0.06 & 1.01 & 310.00 \\
gamma[20,2,3] & 0.02 & 0.03 & 0.00 & 0.00 & 0.01 & 0.03 & 0.11 & 1.08 & 69.00 \\
gamma[21,2,3] & 0.01 & 0.02 & 0.00 & 0.00 & 0.01 & 0.02 & 0.07 & 1.01 & 310.00 \\
gamma[22,2,3] & 0.02 & 0.02 & 0.00 & 0.00 & 0.01 & 0.03 & 0.09 & 1.05 & 280.00 \\
gamma[23,2,3] & 0.19 & 0.05 & 0.10 & 0.16 & 0.19 & 0.22 & 0.28 & 1.01 & 210.00 \\
gamma[24,2,3] & 0.02 & 0.03 & 0.00 & 0.00 & 0.01 & 0.03 & 0.10 & 1.03 & 310.00 \\
gamma[25,2,3] & 0.37 & 0.06 & 0.26 & 0.33 & 0.37 & 0.41 & 0.49 & 1.00 & 1500.00 \\
gamma[1,3,3] & 0.95 & 0.02 & 0.90 & 0.93 & 0.95 & 0.97 & 0.99 & 1.00 & 1100.00 \\
gamma[2,3,3] & 0.97 & 0.02 & 0.92 & 0.95 & 0.97 & 0.98 & 1.00 & 1.00 & 1100.00 \\
gamma[3,3,3] & 0.97 & 0.02 & 0.92 & 0.96 & 0.97 & 0.98 & 1.00 & 1.00 & 650.00 \\
gamma[4,3,3] & 0.83 & 0.06 & 0.71 & 0.79 & 0.83 & 0.87 & 0.95 & 1.04 & 70.00 \\
gamma[5,3,3] & 0.88 & 0.04 & 0.79 & 0.85 & 0.88 & 0.91 & 0.96 & 1.02 & 160.00 \\
gamma[6,3,3] & 0.96 & 0.02 & 0.90 & 0.94 & 0.96 & 0.98 & 1.00 & 1.02 & 230.00 \\
gamma[7,3,3] & 0.98 & 0.01 & 0.94 & 0.97 & 0.98 & 0.99 & 1.00 & 1.01 & 430.00 \\
gamma[8,3,3] & 0.77 & 0.06 & 0.66 & 0.73 & 0.77 & 0.81 & 0.87 & 1.00 & 2300.00 \\
gamma[9,3,3] & 0.67 & 0.16 & 0.37 & 0.55 & 0.67 & 0.79 & 0.97 & 1.01 & 200.00 \\
gamma[10,3,3] & 0.93 & 0.06 & 0.79 & 0.90 & 0.95 & 0.98 & 1.00 & 1.00 & 1400.00 \\
gamma[11,3,3] & 0.93 & 0.06 & 0.78 & 0.91 & 0.95 & 0.98 & 1.00 & 1.01 & 340.00 \\
gamma[12,3,3] & 0.65 & 0.08 & 0.50 & 0.60 & 0.66 & 0.71 & 0.81 & 1.00 & 660.00 \\
gamma[13,3,3] & 0.67 & 0.07 & 0.53 & 0.62 & 0.67 & 0.71 & 0.80 & 1.00 & 2900.00 \\
gamma[14,3,3] & 0.94 & 0.02 & 0.90 & 0.93 & 0.95 & 0.96 & 0.98 & 1.01 & 220.00 \\
gamma[15,3,3] & 0.90 & 0.09 & 0.69 & 0.84 & 0.92 & 0.97 & 1.00 & 1.00 & 950.00 \\
gamma[16,3,3] & 0.99 & 0.00 & 0.98 & 0.99 & 1.00 & 1.00 & 1.00 & 1.01 & 630.00 \\
gamma[17,3,3] & 0.97 & 0.02 & 0.93 & 0.96 & 0.97 & 0.98 & 0.99 & 1.00 & 1100.00 \\
gamma[18,3,3] & 0.95 & 0.02 & 0.91 & 0.94 & 0.95 & 0.96 & 0.98 & 1.02 & 160.00 \\
gamma[19,3,3] & 0.87 & 0.06 & 0.76 & 0.84 & 0.88 & 0.92 & 0.97 & 1.03 & 100.00 \\
gamma[20,3,3] & 0.94 & 0.04 & 0.85 & 0.91 & 0.94 & 0.97 & 1.00 & 1.02 & 170.00 \\
gamma[21,3,3] & 0.71 & 0.10 & 0.52 & 0.63 & 0.70 & 0.77 & 0.91 & 1.03 & 87.00 \\
gamma[22,3,3] & 0.74 & 0.06 & 0.62 & 0.70 & 0.74 & 0.78 & 0.85 & 1.01 & 530.00 \\
gamma[23,3,3] & 0.94 & 0.05 & 0.80 & 0.92 & 0.96 & 0.98 & 1.00 & 1.01 & 1500.00 \\
gamma[24,3,3] & 0.73 & 0.07 & 0.59 & 0.68 & 0.73 & 0.77 & 0.87 & 1.00 & 590.00 \\
gamma[25,3,3] & 0.93 & 0.06 & 0.77 & 0.90 & 0.95 & 0.98 & 1.00 & 1.01 & 450.00 \\
inv.phi[1,1] & 3.37 & 1.62 & 0.95 & 2.21 & 3.12 & 4.25 & 7.21 & 1.10 & 35.00 \\
inv.phi[2,1] & -0.43 & 1.05 & -2.55 & -1.13 & -0.39 & 0.29 & 1.49 & 1.02 & 170.00 \\
inv.phi[3,1] & -1.27 & 1.14 & -3.93 & -1.90 & -1.15 & -0.48 & 0.57 & 1.01 & 220.00 \\
inv.phi[4,1] & -1.27 & 1.20 & -3.90 & -1.97 & -1.15 & -0.48 & 0.76 & 1.02 & 240.00 \\
inv.phi[1,2] & -0.43 & 1.05 & -2.55 & -1.13 & -0.39 & 0.29 & 1.49 & 1.02 & 170.00 \\
inv.phi[2,2] & 2.94 & 1.43 & 0.88 & 1.88 & 2.73 & 3.74 & 6.38 & 1.02 & 270.00 \\
inv.phi[3,2] & -0.25 & 1.01 & -2.25 & -0.89 & -0.28 & 0.37 & 1.78 & 1.02 & 180.00 \\
inv.phi[4,2] & -1.83 & 1.41 & -5.25 & -2.57 & -1.60 & -0.83 & 0.33 & 1.02 & 150.00 \\
inv.phi[1,3] & -1.27 & 1.14 & -3.93 & -1.90 & -1.15 & -0.48 & 0.57 & 1.01 & 220.00 \\
inv.phi[2,3] & -0.25 & 1.01 & -2.25 & -0.89 & -0.28 & 0.37 & 1.78 & 1.02 & 180.00 \\
inv.phi[3,3] & 2.64 & 1.42 & 0.64 & 1.60 & 2.37 & 3.38 & 6.16 & 1.02 & 260.00 \\
inv.phi[4,3] & -0.76 & 1.19 & -3.41 & -1.50 & -0.62 & 0.11 & 1.21 & 1.01 & 320.00 \\
inv.phi[1,4] & -1.27 & 1.20 & -3.90 & -1.97 & -1.15 & -0.48 & 0.76 & 1.02 & 240.00 \\
inv.phi[2,4] & -1.83 & 1.41 & -5.25 & -2.57 & -1.60 & -0.83 & 0.33 & 1.02 & 150.00 \\
inv.phi[3,4] & -0.76 & 1.19 & -3.41 & -1.50 & -0.62 & 0.11 & 1.21 & 1.01 & 320.00 \\
inv.phi[4,4] & 3.54 & 2.01 & 0.76 & 2.05 & 3.11 & 4.65 & 8.52 & 1.03 & 120.00 \\
lambda[1] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
lambda[2] & 1.37 & 0.23 & 1.00 & 1.20 & 1.34 & 1.52 & 1.83 & 1.03 & 110.00 \\
lambda[3] & 1.25 & 0.21 & 0.88 & 1.11 & 1.24 & 1.39 & 1.68 & 1.02 & 130.00 \\
lambda[4] & 1.15 & 0.22 & 0.74 & 1.00 & 1.14 & 1.30 & 1.61 & 1.06 & 50.00 \\
lambda[5] & 1.47 & 0.32 & 0.93 & 1.25 & 1.46 & 1.66 & 2.20 & 1.08 & 39.00 \\
lambda[6] & 1.21 & 0.24 & 0.81 & 1.02 & 1.17 & 1.37 & 1.73 & 1.09 & 35.00 \\
lambda[7] & 1.45 & 0.26 & 0.93 & 1.29 & 1.44 & 1.62 & 1.96 & 1.09 & 33.00 \\
lambda[8] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
lambda[9] & 0.71 & 0.09 & 0.54 & 0.64 & 0.71 & 0.77 & 0.91 & 1.01 & 180.00 \\
lambda[10] & 0.59 & 0.08 & 0.45 & 0.53 & 0.58 & 0.64 & 0.76 & 1.02 & 160.00 \\
lambda[11] & 0.61 & 0.08 & 0.45 & 0.56 & 0.61 & 0.67 & 0.78 & 1.01 & 1000.00 \\
lambda[12] & 0.94 & 0.14 & 0.69 & 0.84 & 0.93 & 1.03 & 1.24 & 1.03 & 78.00 \\
lambda[13] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
lambda[14] & 1.13 & 0.27 & 0.63 & 0.95 & 1.12 & 1.29 & 1.70 & 1.07 & 44.00 \\
lambda[15] & 0.44 & 0.10 & 0.30 & 0.37 & 0.42 & 0.49 & 0.67 & 1.01 & 310.00 \\
lambda[16] & 1.28 & 0.23 & 0.86 & 1.10 & 1.26 & 1.43 & 1.77 & 1.13 & 25.00 \\
lambda[17] & 1.24 & 0.24 & 0.79 & 1.06 & 1.23 & 1.40 & 1.73 & 1.08 & 39.00 \\
lambda[18] & 1.36 & 0.29 & 0.89 & 1.16 & 1.34 & 1.53 & 2.03 & 1.05 & 160.00 \\
lambda[19] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
lambda[20] & 1.03 & 0.15 & 0.76 & 0.93 & 1.02 & 1.12 & 1.36 & 1.07 & 43.00 \\
lambda[21] & 0.87 & 0.14 & 0.63 & 0.76 & 0.86 & 0.96 & 1.16 & 1.08 & 42.00 \\
lambda[22] & 1.12 & 0.14 & 0.87 & 1.02 & 1.11 & 1.21 & 1.43 & 1.02 & 180.00 \\
lambda[23] & 0.78 & 0.10 & 0.59 & 0.71 & 0.78 & 0.84 & 0.99 & 1.01 & 300.00 \\
lambda[24] & 0.87 & 0.12 & 0.66 & 0.79 & 0.86 & 0.93 & 1.13 & 1.02 & 140.00 \\
lambda[25] & 0.71 & 0.10 & 0.54 & 0.64 & 0.71 & 0.77 & 0.91 & 1.01 & 290.00 \\
lambda.std[1] & 0.71 & 0.00 & 0.71 & 0.71 & 0.71 & 0.71 & 0.71 & 1.00 & 1.00 \\
lambda.std[2] & 0.80 & 0.05 & 0.71 & 0.77 & 0.80 & 0.84 & 0.88 & 1.03 & 97.00 \\
lambda.std[3] & 0.77 & 0.05 & 0.66 & 0.74 & 0.78 & 0.81 & 0.86 & 1.03 & 120.00 \\
lambda.std[4] & 0.74 & 0.07 & 0.59 & 0.71 & 0.75 & 0.79 & 0.85 & 1.07 & 47.00 \\
lambda.std[5] & 0.82 & 0.06 & 0.68 & 0.78 & 0.83 & 0.86 & 0.91 & 1.09 & 38.00 \\
lambda.std[6] & 0.76 & 0.06 & 0.63 & 0.72 & 0.76 & 0.81 & 0.87 & 1.08 & 38.00 \\
lambda.std[7] & 0.81 & 0.05 & 0.68 & 0.79 & 0.82 & 0.85 & 0.89 & 1.08 & 39.00 \\
lambda.std[8] & 0.71 & 0.00 & 0.71 & 0.71 & 0.71 & 0.71 & 0.71 & 1.00 & 1.00 \\
lambda.std[9] & 0.57 & 0.05 & 0.47 & 0.54 & 0.58 & 0.61 & 0.67 & 1.01 & 190.00 \\
lambda.std[10] & 0.50 & 0.05 & 0.41 & 0.47 & 0.50 & 0.54 & 0.61 & 1.02 & 160.00 \\
lambda.std[11] & 0.52 & 0.05 & 0.41 & 0.49 & 0.52 & 0.56 & 0.61 & 1.01 & 880.00 \\
lambda.std[12] & 0.68 & 0.05 & 0.57 & 0.64 & 0.68 & 0.72 & 0.78 & 1.03 & 82.00 \\
lambda.std[13] & 0.71 & 0.00 & 0.71 & 0.71 & 0.71 & 0.71 & 0.71 & 1.00 & 1.00 \\
lambda.std[14] & 0.73 & 0.08 & 0.53 & 0.69 & 0.75 & 0.79 & 0.86 & 1.07 & 44.00 \\
lambda.std[15] & 0.40 & 0.07 & 0.29 & 0.35 & 0.39 & 0.44 & 0.56 & 1.01 & 290.00 \\
lambda.std[16] & 0.78 & 0.06 & 0.65 & 0.74 & 0.78 & 0.82 & 0.87 & 1.13 & 26.00 \\
lambda.std[17] & 0.77 & 0.06 & 0.62 & 0.73 & 0.78 & 0.81 & 0.87 & 1.06 & 48.00 \\
lambda.std[18] & 0.79 & 0.06 & 0.66 & 0.76 & 0.80 & 0.84 & 0.90 & 1.03 & 370.00 \\
lambda.std[19] & 0.71 & 0.00 & 0.71 & 0.71 & 0.71 & 0.71 & 0.71 & 1.00 & 1.00 \\
lambda.std[20] & 0.71 & 0.05 & 0.60 & 0.68 & 0.71 & 0.75 & 0.81 & 1.07 & 42.00 \\
lambda.std[21] & 0.65 & 0.06 & 0.54 & 0.61 & 0.65 & 0.69 & 0.76 & 1.07 & 46.00 \\
lambda.std[22] & 0.74 & 0.04 & 0.66 & 0.71 & 0.74 & 0.77 & 0.82 & 1.02 & 180.00 \\
lambda.std[23] & 0.61 & 0.05 & 0.51 & 0.58 & 0.61 & 0.64 & 0.70 & 1.01 & 310.00 \\
lambda.std[24] & 0.65 & 0.05 & 0.55 & 0.62 & 0.65 & 0.68 & 0.75 & 1.02 & 150.00 \\
lambda.std[25] & 0.58 & 0.05 & 0.47 & 0.54 & 0.58 & 0.61 & 0.67 & 1.01 & 290.00 \\
phi[1,1] & 3.60 & 2.30 & 1.01 & 2.18 & 3.04 & 4.43 & 9.33 & 1.08 & 49.00 \\
phi[2,1] & 3.06 & 1.89 & 0.75 & 1.86 & 2.69 & 3.75 & 7.65 & 1.06 & 58.00 \\
phi[3,1] & 3.12 & 2.25 & 0.50 & 1.74 & 2.62 & 3.80 & 9.61 & 1.11 & 52.00 \\
phi[4,1] & 3.40 & 1.32 & 1.15 & 2.56 & 3.26 & 4.10 & 6.38 & 1.10 & 49.00 \\
phi[1,2] & 3.06 & 1.89 & 0.75 & 1.86 & 2.69 & 3.75 & 7.65 & 1.06 & 58.00 \\
phi[2,2] & 4.06 & 2.46 & 1.06 & 2.30 & 3.49 & 5.15 & 10.60 & 1.04 & 67.00 \\
phi[3,2] & 3.07 & 2.15 & 0.37 & 1.61 & 2.59 & 4.02 & 9.02 & 1.13 & 35.00 \\
phi[4,2] & 3.67 & 1.51 & 1.04 & 2.69 & 3.53 & 4.49 & 7.17 & 1.06 & 50.00 \\
phi[1,3] & 3.12 & 2.25 & 0.50 & 1.74 & 2.62 & 3.80 & 9.61 & 1.11 & 52.00 \\
phi[2,3] & 3.07 & 2.15 & 0.37 & 1.61 & 2.59 & 4.02 & 9.02 & 1.13 & 35.00 \\
phi[3,3] & 4.04 & 2.95 & 0.84 & 2.03 & 3.28 & 4.97 & 12.54 & 1.09 & 39.00 \\
phi[4,3] & 3.33 & 1.66 & 0.21 & 2.16 & 3.26 & 4.30 & 7.11 & 1.10 & 37.00 \\
phi[1,4] & 3.40 & 1.32 & 1.15 & 2.56 & 3.26 & 4.10 & 6.38 & 1.10 & 49.00 \\
phi[2,4] & 3.67 & 1.51 & 1.04 & 2.69 & 3.53 & 4.49 & 7.17 & 1.06 & 50.00 \\
phi[3,4] & 3.33 & 1.66 & 0.21 & 2.16 & 3.26 & 4.30 & 7.11 & 1.10 & 37.00 \\
phi[4,4] & 4.48 & 0.67 & 3.44 & 4.02 & 4.36 & 4.81 & 6.15 & 1.06 & 87.00 \\
phi.cor[1,1] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
phi.cor[2,1] & 0.82 & 0.13 & 0.44 & 0.76 & 0.86 & 0.91 & 0.96 & 1.04 & 140.00 \\
phi.cor[3,1] & 0.82 & 0.18 & 0.33 & 0.78 & 0.88 & 0.93 & 0.97 & 1.08 & 110.00 \\
phi.cor[4,1] & 0.86 & 0.13 & 0.47 & 0.84 & 0.91 & 0.94 & 0.97 & 1.06 & 84.00 \\
phi.cor[1,2] & 0.82 & 0.13 & 0.44 & 0.76 & 0.86 & 0.91 & 0.96 & 1.04 & 140.00 \\
phi.cor[2,2] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
phi.cor[3,2] & 0.77 & 0.20 & 0.22 & 0.71 & 0.83 & 0.90 & 0.97 & 1.06 & 94.00 \\
phi.cor[4,2] & 0.87 & 0.16 & 0.46 & 0.86 & 0.92 & 0.95 & 0.98 & 1.21 & 45.00 \\
phi.cor[1,3] & 0.82 & 0.18 & 0.33 & 0.78 & 0.88 & 0.93 & 0.97 & 1.08 & 110.00 \\
phi.cor[2,3] & 0.77 & 0.20 & 0.22 & 0.71 & 0.83 & 0.90 & 0.97 & 1.06 & 94.00 \\
phi.cor[3,3] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
phi.cor[4,3] & 0.80 & 0.22 & 0.10 & 0.75 & 0.88 & 0.93 & 0.97 & 1.07 & 73.00 \\
phi.cor[1,4] & 0.86 & 0.13 & 0.47 & 0.84 & 0.91 & 0.94 & 0.97 & 1.06 & 84.00 \\
phi.cor[2,4] & 0.87 & 0.16 & 0.46 & 0.86 & 0.92 & 0.95 & 0.98 & 1.21 & 45.00 \\
phi.cor[3,4] & 0.80 & 0.22 & 0.10 & 0.75 & 0.88 & 0.93 & 0.97 & 1.07 & 73.00 \\
phi.cor[4,4] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
reli.omega[1] & 0.92 & 0.02 & 0.88 & 0.91 & 0.92 & 0.93 & 0.94 & 1.12 & 28.00 \\
reli.omega[2] & 0.80 & 0.02 & 0.76 & 0.78 & 0.80 & 0.81 & 0.83 & 1.04 & 83.00 \\
reli.omega[3] & 0.87 & 0.02 & 0.83 & 0.86 & 0.87 & 0.89 & 0.91 & 1.07 & 47.00 \\
reli.omega[4] & 0.84 & 0.02 & 0.80 & 0.83 & 0.84 & 0.85 & 0.87 & 1.04 & 80.00 \\
tau[1,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[2,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[3,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[4,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[5,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[6,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[7,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[8,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[9,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[10,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[11,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[12,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[13,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[14,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[15,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[16,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[17,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[18,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[19,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[20,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[21,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[22,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[23,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[24,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[25,1] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 \\
tau[1,2] & 3.89 & 0.32 & 3.33 & 3.67 & 3.86 & 4.09 & 4.62 & 1.03 & 140.00 \\
tau[2,2] & 4.80 & 0.72 & 3.67 & 4.24 & 4.70 & 5.32 & 6.25 & 1.02 & 110.00 \\
tau[3,2] & 4.22 & 0.58 & 3.25 & 3.81 & 4.16 & 4.59 & 5.45 & 1.02 & 230.00 \\
tau[4,2] & 3.51 & 0.72 & 2.25 & 2.99 & 3.49 & 3.98 & 5.02 & 1.07 & 43.00 \\
tau[5,2] & 4.64 & 0.97 & 3.08 & 3.88 & 4.59 & 5.26 & 6.69 & 1.06 & 49.00 \\
tau[6,2] & 4.17 & 0.73 & 3.03 & 3.63 & 4.07 & 4.61 & 5.79 & 1.07 & 51.00 \\
tau[7,2] & 5.43 & 0.76 & 4.02 & 4.91 & 5.38 & 5.93 & 7.03 & 1.06 & 47.00 \\
tau[8,2] & 3.10 & 0.33 & 2.53 & 2.87 & 3.08 & 3.30 & 3.80 & 1.03 & 100.00 \\
tau[9,2] & 1.13 & 0.25 & 0.64 & 0.98 & 1.13 & 1.28 & 1.63 & 1.01 & 320.00 \\
tau[10,2] & 1.54 & 0.15 & 1.29 & 1.43 & 1.52 & 1.63 & 1.88 & 1.00 & 3600.00 \\
tau[11,2] & 0.06 & 0.05 & 0.00 & 0.02 & 0.04 & 0.08 & 0.17 & 1.00 & 4000.00 \\
tau[12,2] & 2.69 & 0.46 & 1.99 & 2.37 & 2.62 & 2.93 & 3.81 & 1.03 & 90.00 \\
tau[13,2] & 2.76 & 0.29 & 2.25 & 2.55 & 2.74 & 2.94 & 3.37 & 1.01 & 230.00 \\
tau[14,2] & 4.57 & 0.86 & 3.09 & 3.97 & 4.50 & 5.11 & 6.47 & 1.06 & 49.00 \\
tau[15,2] & 1.72 & 0.57 & 1.20 & 1.40 & 1.56 & 1.84 & 3.08 & 1.02 & 820.00 \\
tau[16,2] & 5.51 & 0.70 & 4.28 & 4.99 & 5.46 & 5.99 & 7.00 & 1.08 & 35.00 \\
tau[17,2] & 4.68 & 0.74 & 3.36 & 4.15 & 4.63 & 5.15 & 6.25 & 1.05 & 62.00 \\
tau[18,2] & 5.25 & 0.86 & 3.84 & 4.68 & 5.19 & 5.68 & 7.47 & 1.08 & 68.00 \\
tau[19,2] & 2.69 & 0.26 & 2.25 & 2.51 & 2.66 & 2.85 & 3.31 & 1.04 & 78.00 \\
tau[20,2] & 3.45 & 0.51 & 2.66 & 3.08 & 3.38 & 3.76 & 4.64 & 1.04 & 62.00 \\
tau[21,2] & 2.43 & 0.48 & 1.72 & 2.06 & 2.34 & 2.74 & 3.49 & 1.06 & 48.00 \\
tau[22,2] & 3.27 & 0.43 & 2.56 & 2.96 & 3.22 & 3.53 & 4.23 & 1.02 & 120.00 \\
tau[23,2] & 0.04 & 0.04 & 0.00 & 0.01 & 0.03 & 0.06 & 0.13 & 1.00 & 4000.00 \\
tau[24,2] & 2.85 & 0.41 & 2.18 & 2.56 & 2.81 & 3.09 & 3.81 & 1.01 & 270.00 \\
tau[25,2] & 0.03 & 0.03 & 0.00 & 0.01 & 0.02 & 0.05 & 0.11 & 1.00 & 3500.00 \\
theta[1] & 2.00 & 0.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 1.00 & 1.00 \\
theta[2] & 2.92 & 0.64 & 2.00 & 2.43 & 2.78 & 3.32 & 4.34 & 1.03 & 110.00 \\
theta[3] & 2.61 & 0.54 & 1.77 & 2.22 & 2.53 & 2.93 & 3.81 & 1.02 & 140.00 \\
theta[4] & 2.38 & 0.53 & 1.54 & 2.00 & 2.31 & 2.70 & 3.60 & 1.06 & 54.00 \\
theta[5] & 3.27 & 1.00 & 1.87 & 2.57 & 3.14 & 3.75 & 5.85 & 1.08 & 40.00 \\
theta[6] & 2.51 & 0.63 & 1.66 & 2.05 & 2.37 & 2.87 & 4.00 & 1.10 & 34.00 \\
theta[7] & 3.17 & 0.75 & 1.87 & 2.66 & 3.08 & 3.62 & 4.83 & 1.10 & 30.00 \\
theta[8] & 2.00 & 0.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 1.00 & 1.00 \\
theta[9] & 1.51 & 0.14 & 1.29 & 1.42 & 1.50 & 1.59 & 1.82 & 1.01 & 180.00 \\
theta[10] & 1.35 & 0.10 & 1.20 & 1.28 & 1.34 & 1.41 & 1.58 & 1.02 & 160.00 \\
theta[11] & 1.38 & 0.10 & 1.20 & 1.31 & 1.38 & 1.45 & 1.61 & 1.00 & 1600.00 \\
theta[12] & 1.90 & 0.27 & 1.47 & 1.70 & 1.87 & 2.07 & 2.53 & 1.04 & 74.00 \\
theta[13] & 2.00 & 0.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 1.00 & 1.00 \\
theta[14] & 2.35 & 0.64 & 1.40 & 1.90 & 2.26 & 2.66 & 3.89 & 1.06 & 45.00 \\
theta[15] & 1.20 & 0.09 & 1.09 & 1.14 & 1.18 & 1.24 & 1.45 & 1.01 & 410.00 \\
theta[16] & 2.68 & 0.62 & 1.74 & 2.22 & 2.58 & 3.05 & 4.13 & 1.13 & 25.00 \\
theta[17] & 2.60 & 0.62 & 1.63 & 2.13 & 2.51 & 2.97 & 3.99 & 1.09 & 34.00 \\
theta[18] & 2.94 & 0.85 & 1.79 & 2.34 & 2.79 & 3.33 & 5.13 & 1.06 & 110.00 \\
theta[19] & 2.00 & 0.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 1.00 & 1.00 \\
theta[20] & 2.08 & 0.32 & 1.57 & 1.86 & 2.04 & 2.26 & 2.86 & 1.06 & 44.00 \\
theta[21] & 1.78 & 0.25 & 1.40 & 1.59 & 1.74 & 1.93 & 2.35 & 1.08 & 39.00 \\
theta[22] & 2.27 & 0.33 & 1.75 & 2.04 & 2.23 & 2.45 & 3.04 & 1.02 & 190.00 \\
theta[23] & 1.62 & 0.16 & 1.35 & 1.50 & 1.60 & 1.71 & 1.97 & 1.01 & 280.00 \\
theta[24] & 1.76 & 0.21 & 1.43 & 1.62 & 1.74 & 1.87 & 2.28 & 1.02 & 130.00 \\
theta[25] & 1.52 & 0.14 & 1.29 & 1.42 & 1.50 & 1.60 & 1.82 & 1.01 & 310.00 \\
\bottomrule
\end{tabular}
\caption{pools Model 1 posterior distribution summary}
\end{table}
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.3.1 car_3.0-10 carData_3.0-4
[4] mvtnorm_1.1-1 LaplacesDemon_16.1.4 runjags_2.2.0-2
[7] lme4_1.1-26 Matrix_1.3-2 sirt_3.9-4
[10] R2jags_0.6-1 rjags_4-12 eRm_1.0-2
[13] diffIRT_1.5 statmod_1.4.35 xtable_1.8-4
[16] kableExtra_1.3.4 lavaan_0.6-7 polycor_0.7-10
[19] bayesplot_1.8.0 ggmcmc_1.5.1.1 coda_0.19-4
[22] data.table_1.14.0 patchwork_1.1.1 forcats_0.5.1
[25] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4
[28] readr_1.4.0 tidyr_1.1.3 tibble_3.1.0
[31] ggplot2_3.3.5 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] minqa_1.2.4 TAM_3.5-19 colorspace_2.0-0 rio_0.5.26
[5] ellipsis_0.3.1 ggridges_0.5.3 rprojroot_2.0.2 fs_1.5.0
[9] rstudioapi_0.13 farver_2.1.0 fansi_0.4.2 lubridate_1.7.10
[13] xml2_1.3.2 splines_4.0.5 mnormt_2.0.2 knitr_1.31
[17] jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.5 dbplyr_2.1.0
[21] compiler_4.0.5 httr_1.4.2 backports_1.2.1 assertthat_0.2.1
[25] cli_2.3.1 later_1.1.0.1 htmltools_0.5.1.1 tools_4.0.5
[29] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4 Rcpp_1.0.7
[33] cellranger_1.1.0 jquerylib_0.1.3 vctrs_0.3.6 svglite_2.0.0
[37] nlme_3.1-152 psych_2.0.12 xfun_0.21 ps_1.6.0
[41] openxlsx_4.2.3 rvest_1.0.0 lifecycle_1.0.0 MASS_7.3-53.1
[45] scales_1.1.1 ragg_1.1.1 hms_1.0.0 promises_1.2.0.1
[49] parallel_4.0.5 RColorBrewer_1.1-2 curl_4.3 yaml_2.2.1
[53] sass_0.3.1 reshape_0.8.8 stringi_1.5.3 highr_0.8
[57] zip_2.1.1 boot_1.3-27 rlang_0.4.10 pkgconfig_2.0.3
[61] systemfonts_1.0.1 evaluate_0.14 lattice_0.20-41 labeling_0.4.2
[65] tidyselect_1.1.0 GGally_2.1.1 plyr_1.8.6 magrittr_2.0.1
[69] R6_2.5.0 generics_0.1.0 DBI_1.1.1 foreign_0.8-81
[73] pillar_1.5.1 haven_2.3.1 withr_2.4.1 abind_1.4-5
[77] modelr_0.1.8 crayon_1.4.1 utf8_1.1.4 tmvnsim_1.0-2
[81] rmarkdown_2.7 grid_4.0.5 CDM_7.5-15 pbivnorm_0.6.0
[85] git2r_0.28.0 reprex_1.0.0 digest_0.6.27 webshot_0.5.2
[89] httpuv_1.5.5 textshaping_0.3.1 stats4_4.0.5 munsell_0.5.0
[93] viridisLite_0.3.0 bslib_0.2.4 R2WinBUGS_2.1-21