SERA POOLS Study
Model 1 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 1: Traditional IFA
Model details
cat(read_file(paste0(w.d, "/code/pools_study/model_ifa.txt")))model {
### Model
for(p in 1:N){
for(i in 1:nit){
# data model
y[p,i] ~ dcat(pi[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])
}
}
### Priors
# 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")
# 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
)
)
model.fit <- R2jags::jags(
model = paste0(w.d, "/code/pools_study/model_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: 12787
Total graph size: 122291
Initializing modelprint(model.fit, width=1000)Inference for Bugs model at "C:/Users/noahp/Documents/GitHub/Padgett-Dissertation/code/pools_study/model_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
inv.phi[1,1] 3.156 1.614 0.760 2.008 2.900 4.000 7.169 1.03 140
inv.phi[2,1] -0.460 1.056 -2.762 -1.056 -0.414 0.219 1.509 1.02 160
inv.phi[3,1] -1.252 1.105 -3.846 -1.855 -1.116 -0.456 0.492 1.01 230
inv.phi[4,1] -1.271 1.433 -4.447 -2.124 -1.135 -0.283 1.152 1.01 310
inv.phi[1,2] -0.460 1.056 -2.762 -1.056 -0.414 0.219 1.509 1.02 160
inv.phi[2,2] 2.869 1.461 0.891 1.777 2.594 3.680 6.307 1.01 250
inv.phi[3,2] -0.135 0.898 -1.976 -0.681 -0.115 0.424 1.689 1.01 490
inv.phi[4,2] -1.766 1.409 -4.945 -2.551 -1.569 -0.786 0.497 1.02 180
inv.phi[1,3] -1.252 1.105 -3.846 -1.855 -1.116 -0.456 0.492 1.01 230
inv.phi[2,3] -0.135 0.898 -1.976 -0.681 -0.115 0.424 1.689 1.01 490
inv.phi[3,3] 2.748 1.319 0.722 1.799 2.512 3.474 5.946 1.02 180
inv.phi[4,3] -1.000 1.135 -3.497 -1.667 -0.889 -0.225 0.910 1.01 280
inv.phi[1,4] -1.271 1.433 -4.447 -2.124 -1.135 -0.283 1.152 1.01 310
inv.phi[2,4] -1.766 1.409 -4.945 -2.551 -1.569 -0.786 0.497 1.02 180
inv.phi[3,4] -1.000 1.135 -3.497 -1.667 -0.889 -0.225 0.910 1.01 280
inv.phi[4,4] 4.019 2.103 0.992 2.441 3.660 5.165 9.062 1.03 130
lambda[1] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
lambda[2] 1.241 0.109 1.034 1.165 1.239 1.315 1.461 1.00 1600
lambda[3] 0.789 0.080 0.640 0.734 0.786 0.839 0.956 1.00 1500
lambda[4] 0.780 0.081 0.632 0.724 0.777 0.835 0.945 1.00 1100
lambda[5] 0.997 0.092 0.831 0.934 0.994 1.056 1.185 1.00 660
lambda[6] 0.916 0.085 0.756 0.858 0.914 0.972 1.094 1.00 1500
lambda[7] 1.000 0.095 0.824 0.933 0.997 1.061 1.195 1.01 380
lambda[8] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
lambda[9] 0.865 0.083 0.709 0.808 0.863 0.920 1.030 1.00 900
lambda[10] 0.769 0.075 0.629 0.717 0.767 0.818 0.923 1.00 1500
lambda[11] 0.732 0.078 0.587 0.679 0.730 0.783 0.893 1.00 3000
lambda[12] 1.025 0.089 0.857 0.965 1.024 1.082 1.205 1.00 3900
lambda[13] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
lambda[14] 0.421 0.064 0.298 0.378 0.419 0.462 0.550 1.00 1300
lambda[15] 0.471 0.062 0.351 0.429 0.471 0.511 0.596 1.00 1300
lambda[16] 0.292 0.062 0.175 0.250 0.292 0.332 0.416 1.00 2100
lambda[17] 0.658 0.074 0.519 0.609 0.656 0.706 0.810 1.00 560
lambda[18] 0.542 0.069 0.414 0.495 0.539 0.587 0.685 1.00 840
lambda[19] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
lambda[20] 1.142 0.099 0.956 1.072 1.141 1.207 1.343 1.00 1400
lambda[21] 0.966 0.085 0.805 0.906 0.965 1.022 1.141 1.00 2400
lambda[22] 1.197 0.104 1.006 1.126 1.194 1.264 1.410 1.00 1200
lambda[23] 0.883 0.087 0.719 0.825 0.880 0.940 1.062 1.00 1700
lambda[24] 1.002 0.087 0.840 0.943 0.999 1.060 1.176 1.00 1600
lambda[25] 0.770 0.079 0.623 0.713 0.769 0.822 0.929 1.00 640
lambda.std[1] 0.707 0.000 0.707 0.707 0.707 0.707 0.707 1.00 1
lambda.std[2] 0.777 0.027 0.719 0.759 0.778 0.796 0.825 1.00 1900
lambda.std[3] 0.617 0.039 0.539 0.592 0.618 0.643 0.691 1.00 1500
lambda.std[4] 0.613 0.040 0.534 0.586 0.614 0.641 0.687 1.00 1100
lambda.std[5] 0.704 0.032 0.639 0.683 0.705 0.726 0.764 1.00 690
lambda.std[6] 0.673 0.034 0.603 0.651 0.675 0.697 0.738 1.00 1400
lambda.std[7] 0.705 0.033 0.636 0.682 0.706 0.728 0.767 1.01 370
lambda.std[8] 0.707 0.000 0.707 0.707 0.707 0.707 0.707 1.00 1
lambda.std[9] 0.652 0.036 0.579 0.628 0.653 0.677 0.718 1.00 830
lambda.std[10] 0.608 0.037 0.532 0.583 0.609 0.633 0.678 1.00 1500
lambda.std[11] 0.588 0.041 0.506 0.562 0.590 0.617 0.666 1.00 3100
lambda.std[12] 0.714 0.030 0.651 0.695 0.715 0.734 0.769 1.00 3600
lambda.std[13] 0.707 0.000 0.707 0.707 0.707 0.707 0.707 1.00 1
lambda.std[14] 0.386 0.050 0.286 0.354 0.386 0.419 0.482 1.00 1400
lambda.std[15] 0.424 0.046 0.331 0.394 0.426 0.455 0.512 1.00 1300
lambda.std[16] 0.279 0.054 0.172 0.243 0.280 0.315 0.384 1.00 2100
lambda.std[17] 0.548 0.043 0.460 0.520 0.549 0.577 0.629 1.00 550
lambda.std[18] 0.474 0.047 0.382 0.443 0.475 0.506 0.565 1.00 860
lambda.std[19] 0.707 0.000 0.707 0.707 0.707 0.707 0.707 1.00 1
lambda.std[20] 0.750 0.029 0.691 0.731 0.752 0.770 0.802 1.00 1300
lambda.std[21] 0.693 0.032 0.627 0.671 0.694 0.715 0.752 1.00 2800
lambda.std[22] 0.765 0.027 0.709 0.748 0.767 0.784 0.816 1.00 1100
lambda.std[23] 0.660 0.037 0.584 0.637 0.660 0.685 0.728 1.00 1800
lambda.std[24] 0.706 0.031 0.643 0.686 0.707 0.728 0.762 1.00 1700
lambda.std[25] 0.608 0.039 0.529 0.580 0.609 0.635 0.681 1.00 660
phi[1,1] 2.621 1.631 0.856 1.636 2.254 3.045 7.216 1.03 170
phi[2,1] 1.583 0.883 0.188 1.006 1.510 2.039 3.640 1.03 1800
phi[3,1] 1.931 1.046 0.465 1.233 1.774 2.384 4.552 1.02 490
phi[4,1] 1.908 0.735 0.526 1.456 1.897 2.322 3.473 1.03 360
phi[1,2] 1.583 0.883 0.188 1.006 1.510 2.039 3.640 1.03 1800
phi[2,2] 2.078 1.022 0.723 1.368 1.900 2.517 4.717 1.02 280
phi[3,2] 1.399 0.818 0.025 0.837 1.314 1.881 3.145 1.02 200
phi[4,2] 1.677 0.701 0.186 1.261 1.715 2.117 3.064 1.02 270
phi[1,3] 1.931 1.046 0.465 1.233 1.774 2.384 4.552 1.02 490
phi[2,3] 1.399 0.818 0.025 0.837 1.314 1.881 3.145 1.02 200
phi[3,3] 2.373 1.199 0.821 1.525 2.127 2.949 5.177 1.00 4000
phi[4,3] 1.781 0.671 0.426 1.336 1.785 2.224 3.074 1.00 550
phi[1,4] 1.908 0.735 0.526 1.456 1.897 2.322 3.473 1.03 360
phi[2,4] 1.677 0.701 0.186 1.261 1.715 2.117 3.064 1.02 270
phi[3,4] 1.781 0.671 0.426 1.336 1.785 2.224 3.074 1.00 550
phi[4,4] 2.290 0.242 1.855 2.124 2.279 2.439 2.811 1.00 1800
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] 0.696 0.210 0.125 0.620 0.752 0.842 0.925 1.03 200
phi.cor[3,1] 0.784 0.157 0.368 0.723 0.833 0.895 0.951 1.01 780
phi.cor[4,1] 0.799 0.172 0.319 0.750 0.860 0.908 0.952 1.03 890
phi.cor[1,2] 0.696 0.210 0.125 0.620 0.752 0.842 0.925 1.03 200
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] 0.642 0.241 0.022 0.533 0.707 0.821 0.914 1.02 170
phi.cor[4,2] 0.769 0.215 0.109 0.724 0.849 0.903 0.952 1.05 210
phi.cor[1,3] 0.784 0.157 0.368 0.723 0.833 0.895 0.951 1.01 780
phi.cor[2,3] 0.642 0.241 0.022 0.533 0.707 0.821 0.914 1.02 170
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] 0.772 0.178 0.237 0.721 0.828 0.887 0.941 1.02 200
phi.cor[1,4] 0.799 0.172 0.319 0.750 0.860 0.908 0.952 1.03 890
phi.cor[2,4] 0.769 0.215 0.109 0.724 0.849 0.903 0.952 1.05 210
phi.cor[3,4] 0.772 0.178 0.237 0.721 0.828 0.887 0.941 1.02 200
phi.cor[4,4] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.00 1
reli.omega[1] 0.865 0.011 0.843 0.858 0.866 0.873 0.886 1.01 430
reli.omega[2] 0.828 0.011 0.807 0.821 0.829 0.836 0.849 1.00 4000
reli.omega[3] 0.655 0.025 0.604 0.638 0.655 0.672 0.703 1.01 410
reli.omega[4] 0.864 0.011 0.842 0.857 0.864 0.872 0.884 1.00 3100
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] 2.842 0.149 2.553 2.743 2.837 2.942 3.138 1.00 3900
tau[2,2] 3.235 0.198 2.868 3.095 3.228 3.368 3.648 1.00 2300
tau[3,2] 2.353 0.152 2.062 2.247 2.349 2.454 2.648 1.00 1800
tau[4,2] 1.780 0.117 1.556 1.699 1.778 1.858 2.011 1.00 770
tau[5,2] 2.298 0.144 2.023 2.195 2.298 2.397 2.584 1.00 840
tau[6,2] 2.586 0.157 2.297 2.475 2.582 2.689 2.909 1.00 2700
tau[7,2] 3.085 0.198 2.709 2.952 3.082 3.217 3.483 1.00 1500
tau[8,2] 1.898 0.106 1.691 1.825 1.898 1.967 2.107 1.00 4000
tau[9,2] 1.095 0.081 0.941 1.040 1.094 1.150 1.258 1.00 1200
tau[10,2] 1.436 0.099 1.245 1.372 1.435 1.501 1.644 1.00 4000
tau[11,2] 0.667 0.054 0.564 0.631 0.665 0.701 0.776 1.00 3100
tau[12,2] 1.697 0.102 1.501 1.626 1.696 1.763 1.902 1.00 3300
tau[13,2] 1.678 0.090 1.505 1.615 1.677 1.740 1.856 1.00 1500
tau[14,2] 1.637 0.132 1.380 1.547 1.638 1.724 1.901 1.00 1000
tau[15,2] 1.273 0.092 1.100 1.212 1.270 1.336 1.455 1.00 2500
tau[16,2] 1.838 0.184 1.492 1.712 1.832 1.961 2.212 1.00 4000
tau[17,2] 2.208 0.155 1.910 2.103 2.205 2.310 2.525 1.00 720
tau[18,2] 2.091 0.147 1.801 1.993 2.090 2.188 2.388 1.00 1400
tau[19,2] 1.954 0.100 1.766 1.884 1.953 2.021 2.153 1.00 2000
tau[20,2] 2.643 0.153 2.352 2.537 2.640 2.745 2.953 1.00 1300
tau[21,2] 1.650 0.103 1.450 1.581 1.648 1.719 1.854 1.00 700
tau[22,2] 2.035 0.125 1.798 1.951 2.030 2.116 2.283 1.00 700
tau[23,2] 0.583 0.051 0.488 0.547 0.582 0.617 0.687 1.00 4000
tau[24,2] 1.907 0.110 1.702 1.832 1.904 1.983 2.132 1.00 1000
tau[25,2] 0.745 0.056 0.641 0.706 0.744 0.783 0.862 1.00 2300
theta[1] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.00 1
theta[2] 2.553 0.272 2.069 2.357 2.536 2.729 3.135 1.00 1400
theta[3] 1.628 0.128 1.409 1.539 1.617 1.704 1.914 1.00 1500
theta[4] 1.615 0.128 1.399 1.524 1.604 1.697 1.893 1.00 1300
theta[5] 2.003 0.185 1.691 1.873 1.989 2.116 2.405 1.00 630
theta[6] 1.847 0.157 1.572 1.736 1.836 1.946 2.198 1.00 1600
theta[7] 2.009 0.192 1.679 1.871 1.995 2.127 2.429 1.01 390
theta[8] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.00 1
theta[9] 1.755 0.145 1.503 1.653 1.744 1.846 2.061 1.00 1000
theta[10] 1.597 0.117 1.395 1.514 1.589 1.670 1.852 1.00 1500
theta[11] 1.542 0.115 1.344 1.461 1.533 1.613 1.797 1.00 2900
theta[12] 2.059 0.184 1.734 1.932 2.048 2.170 2.451 1.00 4000
theta[13] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.00 1
theta[14] 1.181 0.055 1.089 1.143 1.175 1.213 1.302 1.00 1300
theta[15] 1.225 0.059 1.123 1.184 1.222 1.261 1.355 1.00 1500
theta[16] 1.089 0.037 1.031 1.063 1.085 1.111 1.173 1.00 3100
theta[17] 1.439 0.099 1.269 1.371 1.430 1.499 1.655 1.00 580
theta[18] 1.298 0.076 1.171 1.245 1.291 1.344 1.470 1.00 810
theta[19] 2.000 0.000 2.000 2.000 2.000 2.000 2.000 1.00 1
theta[20] 2.313 0.228 1.913 2.150 2.302 2.457 2.805 1.00 1500
theta[21] 1.941 0.166 1.648 1.821 1.931 2.044 2.301 1.00 2100
theta[22] 2.445 0.253 2.013 2.267 2.425 2.597 2.987 1.00 1300
theta[23] 1.788 0.155 1.517 1.681 1.774 1.884 2.127 1.00 1500
theta[24] 2.012 0.175 1.705 1.889 1.999 2.125 2.383 1.00 1500
theta[25] 1.599 0.123 1.388 1.508 1.591 1.676 1.863 1.00 610
deviance 15091.744 119.045 14858.102 15012.531 15091.659 15170.140 15327.725 1.00 4000
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 = 7086.0 and DIC = 22177.7
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 | 15091.744 | 119.045 | 14858.102 | 15012.531 | 15091.659 | 15170.140 | 15327.725 | 1.00 | 4000 |
| inv.phi[1,1] | 3.156 | 1.614 | 0.760 | 2.008 | 2.900 | 4.000 | 7.169 | 1.03 | 140 |
| inv.phi[2,1] | -0.460 | 1.056 | -2.762 | -1.056 | -0.414 | 0.219 | 1.509 | 1.02 | 160 |
| inv.phi[3,1] | -1.252 | 1.105 | -3.846 | -1.855 | -1.116 | -0.456 | 0.492 | 1.01 | 230 |
| inv.phi[4,1] | -1.271 | 1.433 | -4.447 | -2.124 | -1.135 | -0.283 | 1.152 | 1.01 | 310 |
| inv.phi[1,2] | -0.460 | 1.056 | -2.762 | -1.056 | -0.414 | 0.219 | 1.509 | 1.02 | 160 |
| inv.phi[2,2] | 2.869 | 1.461 | 0.891 | 1.777 | 2.594 | 3.680 | 6.307 | 1.01 | 250 |
| inv.phi[3,2] | -0.135 | 0.898 | -1.976 | -0.681 | -0.115 | 0.424 | 1.689 | 1.01 | 490 |
| inv.phi[4,2] | -1.766 | 1.409 | -4.945 | -2.551 | -1.569 | -0.786 | 0.497 | 1.02 | 180 |
| inv.phi[1,3] | -1.252 | 1.105 | -3.846 | -1.855 | -1.116 | -0.456 | 0.492 | 1.01 | 230 |
| inv.phi[2,3] | -0.135 | 0.898 | -1.976 | -0.681 | -0.115 | 0.424 | 1.689 | 1.01 | 490 |
| inv.phi[3,3] | 2.748 | 1.319 | 0.722 | 1.799 | 2.512 | 3.474 | 5.946 | 1.02 | 180 |
| inv.phi[4,3] | -1.000 | 1.135 | -3.497 | -1.667 | -0.889 | -0.225 | 0.910 | 1.01 | 280 |
| inv.phi[1,4] | -1.271 | 1.433 | -4.447 | -2.124 | -1.135 | -0.283 | 1.152 | 1.01 | 310 |
| inv.phi[2,4] | -1.766 | 1.409 | -4.945 | -2.551 | -1.569 | -0.786 | 0.497 | 1.02 | 180 |
| inv.phi[3,4] | -1.000 | 1.135 | -3.497 | -1.667 | -0.889 | -0.225 | 0.910 | 1.01 | 280 |
| inv.phi[4,4] | 4.019 | 2.103 | 0.992 | 2.441 | 3.660 | 5.165 | 9.062 | 1.03 | 130 |
| lambda[1] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| lambda[2] | 1.241 | 0.109 | 1.034 | 1.165 | 1.239 | 1.315 | 1.461 | 1.00 | 1600 |
| lambda[3] | 0.789 | 0.080 | 0.640 | 0.734 | 0.786 | 0.839 | 0.956 | 1.00 | 1500 |
| lambda[4] | 0.780 | 0.081 | 0.632 | 0.724 | 0.777 | 0.835 | 0.945 | 1.00 | 1100 |
| lambda[5] | 0.997 | 0.092 | 0.831 | 0.934 | 0.994 | 1.056 | 1.185 | 1.00 | 660 |
| lambda[6] | 0.916 | 0.085 | 0.756 | 0.858 | 0.914 | 0.972 | 1.094 | 1.00 | 1500 |
| lambda[7] | 1.000 | 0.095 | 0.824 | 0.933 | 0.997 | 1.061 | 1.195 | 1.01 | 380 |
| lambda[8] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| lambda[9] | 0.865 | 0.083 | 0.709 | 0.808 | 0.863 | 0.920 | 1.030 | 1.00 | 900 |
| lambda[10] | 0.769 | 0.075 | 0.629 | 0.717 | 0.767 | 0.818 | 0.923 | 1.00 | 1500 |
| lambda[11] | 0.732 | 0.078 | 0.587 | 0.679 | 0.730 | 0.783 | 0.893 | 1.00 | 3000 |
| lambda[12] | 1.025 | 0.089 | 0.857 | 0.965 | 1.024 | 1.082 | 1.205 | 1.00 | 3900 |
| lambda[13] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| lambda[14] | 0.421 | 0.064 | 0.298 | 0.378 | 0.419 | 0.462 | 0.550 | 1.00 | 1300 |
| lambda[15] | 0.471 | 0.062 | 0.351 | 0.429 | 0.471 | 0.511 | 0.596 | 1.00 | 1300 |
| lambda[16] | 0.292 | 0.062 | 0.175 | 0.250 | 0.292 | 0.332 | 0.416 | 1.00 | 2100 |
| lambda[17] | 0.658 | 0.074 | 0.519 | 0.609 | 0.656 | 0.706 | 0.810 | 1.00 | 560 |
| lambda[18] | 0.542 | 0.069 | 0.414 | 0.495 | 0.539 | 0.587 | 0.685 | 1.00 | 840 |
| lambda[19] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| lambda[20] | 1.142 | 0.099 | 0.956 | 1.072 | 1.141 | 1.207 | 1.343 | 1.00 | 1400 |
| lambda[21] | 0.966 | 0.085 | 0.805 | 0.906 | 0.965 | 1.022 | 1.141 | 1.00 | 2400 |
| lambda[22] | 1.197 | 0.104 | 1.006 | 1.126 | 1.194 | 1.264 | 1.410 | 1.00 | 1200 |
| lambda[23] | 0.883 | 0.087 | 0.719 | 0.825 | 0.880 | 0.940 | 1.062 | 1.00 | 1700 |
| lambda[24] | 1.002 | 0.087 | 0.840 | 0.943 | 0.999 | 1.060 | 1.176 | 1.00 | 1600 |
| lambda[25] | 0.770 | 0.079 | 0.623 | 0.713 | 0.769 | 0.822 | 0.929 | 1.00 | 640 |
| lambda.std[1] | 0.707 | 0.000 | 0.707 | 0.707 | 0.707 | 0.707 | 0.707 | 1.00 | 1 |
| lambda.std[2] | 0.777 | 0.027 | 0.719 | 0.759 | 0.778 | 0.796 | 0.825 | 1.00 | 1900 |
| lambda.std[3] | 0.617 | 0.039 | 0.539 | 0.592 | 0.618 | 0.643 | 0.691 | 1.00 | 1500 |
| lambda.std[4] | 0.613 | 0.040 | 0.534 | 0.586 | 0.614 | 0.641 | 0.687 | 1.00 | 1100 |
| lambda.std[5] | 0.704 | 0.032 | 0.639 | 0.683 | 0.705 | 0.726 | 0.764 | 1.00 | 690 |
| lambda.std[6] | 0.673 | 0.034 | 0.603 | 0.651 | 0.675 | 0.697 | 0.738 | 1.00 | 1400 |
| lambda.std[7] | 0.705 | 0.033 | 0.636 | 0.682 | 0.706 | 0.728 | 0.767 | 1.01 | 370 |
| lambda.std[8] | 0.707 | 0.000 | 0.707 | 0.707 | 0.707 | 0.707 | 0.707 | 1.00 | 1 |
| lambda.std[9] | 0.652 | 0.036 | 0.579 | 0.628 | 0.653 | 0.677 | 0.718 | 1.00 | 830 |
| lambda.std[10] | 0.608 | 0.037 | 0.532 | 0.583 | 0.609 | 0.633 | 0.678 | 1.00 | 1500 |
| lambda.std[11] | 0.588 | 0.041 | 0.506 | 0.562 | 0.590 | 0.617 | 0.666 | 1.00 | 3100 |
| lambda.std[12] | 0.714 | 0.030 | 0.651 | 0.695 | 0.715 | 0.734 | 0.769 | 1.00 | 3600 |
| lambda.std[13] | 0.707 | 0.000 | 0.707 | 0.707 | 0.707 | 0.707 | 0.707 | 1.00 | 1 |
| lambda.std[14] | 0.386 | 0.050 | 0.286 | 0.354 | 0.386 | 0.419 | 0.482 | 1.00 | 1400 |
| lambda.std[15] | 0.424 | 0.046 | 0.331 | 0.394 | 0.426 | 0.455 | 0.512 | 1.00 | 1300 |
| lambda.std[16] | 0.279 | 0.054 | 0.172 | 0.243 | 0.280 | 0.315 | 0.384 | 1.00 | 2100 |
| lambda.std[17] | 0.548 | 0.043 | 0.460 | 0.520 | 0.549 | 0.577 | 0.629 | 1.00 | 550 |
| lambda.std[18] | 0.474 | 0.047 | 0.382 | 0.443 | 0.475 | 0.506 | 0.565 | 1.00 | 860 |
| lambda.std[19] | 0.707 | 0.000 | 0.707 | 0.707 | 0.707 | 0.707 | 0.707 | 1.00 | 1 |
| lambda.std[20] | 0.750 | 0.029 | 0.691 | 0.731 | 0.752 | 0.770 | 0.802 | 1.00 | 1300 |
| lambda.std[21] | 0.693 | 0.032 | 0.627 | 0.671 | 0.694 | 0.715 | 0.752 | 1.00 | 2800 |
| lambda.std[22] | 0.765 | 0.027 | 0.709 | 0.748 | 0.767 | 0.784 | 0.816 | 1.00 | 1100 |
| lambda.std[23] | 0.660 | 0.037 | 0.584 | 0.637 | 0.660 | 0.685 | 0.728 | 1.00 | 1800 |
| lambda.std[24] | 0.706 | 0.031 | 0.643 | 0.686 | 0.707 | 0.728 | 0.762 | 1.00 | 1700 |
| lambda.std[25] | 0.608 | 0.039 | 0.529 | 0.580 | 0.609 | 0.635 | 0.681 | 1.00 | 660 |
| phi[1,1] | 2.621 | 1.631 | 0.856 | 1.636 | 2.254 | 3.045 | 7.216 | 1.03 | 170 |
| phi[2,1] | 1.583 | 0.883 | 0.188 | 1.006 | 1.510 | 2.039 | 3.640 | 1.03 | 1800 |
| phi[3,1] | 1.931 | 1.046 | 0.465 | 1.233 | 1.774 | 2.384 | 4.552 | 1.02 | 490 |
| phi[4,1] | 1.908 | 0.735 | 0.526 | 1.456 | 1.897 | 2.322 | 3.473 | 1.03 | 360 |
| phi[1,2] | 1.583 | 0.883 | 0.188 | 1.006 | 1.510 | 2.039 | 3.640 | 1.03 | 1800 |
| phi[2,2] | 2.078 | 1.022 | 0.723 | 1.368 | 1.900 | 2.517 | 4.717 | 1.02 | 280 |
| phi[3,2] | 1.399 | 0.818 | 0.025 | 0.837 | 1.314 | 1.881 | 3.145 | 1.02 | 200 |
| phi[4,2] | 1.677 | 0.701 | 0.186 | 1.261 | 1.715 | 2.117 | 3.064 | 1.02 | 270 |
| phi[1,3] | 1.931 | 1.046 | 0.465 | 1.233 | 1.774 | 2.384 | 4.552 | 1.02 | 490 |
| phi[2,3] | 1.399 | 0.818 | 0.025 | 0.837 | 1.314 | 1.881 | 3.145 | 1.02 | 200 |
| phi[3,3] | 2.373 | 1.199 | 0.821 | 1.525 | 2.127 | 2.949 | 5.177 | 1.00 | 4000 |
| phi[4,3] | 1.781 | 0.671 | 0.426 | 1.336 | 1.785 | 2.224 | 3.074 | 1.00 | 550 |
| phi[1,4] | 1.908 | 0.735 | 0.526 | 1.456 | 1.897 | 2.322 | 3.473 | 1.03 | 360 |
| phi[2,4] | 1.677 | 0.701 | 0.186 | 1.261 | 1.715 | 2.117 | 3.064 | 1.02 | 270 |
| phi[3,4] | 1.781 | 0.671 | 0.426 | 1.336 | 1.785 | 2.224 | 3.074 | 1.00 | 550 |
| phi[4,4] | 2.290 | 0.242 | 1.855 | 2.124 | 2.279 | 2.439 | 2.811 | 1.00 | 1800 |
| 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] | 0.696 | 0.210 | 0.125 | 0.620 | 0.752 | 0.842 | 0.925 | 1.03 | 200 |
| phi.cor[3,1] | 0.784 | 0.157 | 0.368 | 0.723 | 0.833 | 0.895 | 0.951 | 1.01 | 780 |
| phi.cor[4,1] | 0.799 | 0.172 | 0.319 | 0.750 | 0.860 | 0.908 | 0.952 | 1.03 | 890 |
| phi.cor[1,2] | 0.696 | 0.210 | 0.125 | 0.620 | 0.752 | 0.842 | 0.925 | 1.03 | 200 |
| 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] | 0.642 | 0.241 | 0.022 | 0.533 | 0.707 | 0.821 | 0.914 | 1.02 | 170 |
| phi.cor[4,2] | 0.769 | 0.215 | 0.109 | 0.724 | 0.849 | 0.903 | 0.952 | 1.05 | 210 |
| phi.cor[1,3] | 0.784 | 0.157 | 0.368 | 0.723 | 0.833 | 0.895 | 0.951 | 1.01 | 780 |
| phi.cor[2,3] | 0.642 | 0.241 | 0.022 | 0.533 | 0.707 | 0.821 | 0.914 | 1.02 | 170 |
| 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] | 0.772 | 0.178 | 0.237 | 0.721 | 0.828 | 0.887 | 0.941 | 1.02 | 200 |
| phi.cor[1,4] | 0.799 | 0.172 | 0.319 | 0.750 | 0.860 | 0.908 | 0.952 | 1.03 | 890 |
| phi.cor[2,4] | 0.769 | 0.215 | 0.109 | 0.724 | 0.849 | 0.903 | 0.952 | 1.05 | 210 |
| phi.cor[3,4] | 0.772 | 0.178 | 0.237 | 0.721 | 0.828 | 0.887 | 0.941 | 1.02 | 200 |
| phi.cor[4,4] | 1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1 |
| reli.omega[1] | 0.865 | 0.011 | 0.843 | 0.858 | 0.866 | 0.873 | 0.886 | 1.01 | 430 |
| reli.omega[2] | 0.828 | 0.011 | 0.807 | 0.821 | 0.829 | 0.836 | 0.849 | 1.00 | 4000 |
| reli.omega[3] | 0.655 | 0.025 | 0.604 | 0.638 | 0.655 | 0.672 | 0.703 | 1.01 | 410 |
| reli.omega[4] | 0.864 | 0.011 | 0.842 | 0.857 | 0.864 | 0.872 | 0.884 | 1.00 | 3100 |
| 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] | 2.842 | 0.149 | 2.553 | 2.743 | 2.837 | 2.942 | 3.138 | 1.00 | 3900 |
| tau[2,2] | 3.235 | 0.198 | 2.868 | 3.095 | 3.228 | 3.368 | 3.648 | 1.00 | 2300 |
| tau[3,2] | 2.353 | 0.152 | 2.062 | 2.247 | 2.349 | 2.454 | 2.648 | 1.00 | 1800 |
| tau[4,2] | 1.780 | 0.117 | 1.556 | 1.699 | 1.778 | 1.858 | 2.011 | 1.00 | 770 |
| tau[5,2] | 2.298 | 0.144 | 2.023 | 2.195 | 2.298 | 2.397 | 2.584 | 1.00 | 840 |
| tau[6,2] | 2.586 | 0.157 | 2.297 | 2.475 | 2.582 | 2.689 | 2.909 | 1.00 | 2700 |
| tau[7,2] | 3.085 | 0.198 | 2.709 | 2.952 | 3.082 | 3.217 | 3.483 | 1.00 | 1500 |
| tau[8,2] | 1.898 | 0.106 | 1.691 | 1.825 | 1.898 | 1.967 | 2.107 | 1.00 | 4000 |
| tau[9,2] | 1.095 | 0.081 | 0.941 | 1.040 | 1.094 | 1.150 | 1.258 | 1.00 | 1200 |
| tau[10,2] | 1.436 | 0.099 | 1.245 | 1.372 | 1.435 | 1.501 | 1.644 | 1.00 | 4000 |
| tau[11,2] | 0.667 | 0.054 | 0.564 | 0.631 | 0.665 | 0.701 | 0.776 | 1.00 | 3100 |
| tau[12,2] | 1.697 | 0.102 | 1.501 | 1.626 | 1.696 | 1.763 | 1.902 | 1.00 | 3300 |
| tau[13,2] | 1.678 | 0.090 | 1.505 | 1.615 | 1.677 | 1.740 | 1.856 | 1.00 | 1500 |
| tau[14,2] | 1.637 | 0.132 | 1.380 | 1.547 | 1.638 | 1.724 | 1.901 | 1.00 | 1000 |
| tau[15,2] | 1.273 | 0.092 | 1.100 | 1.212 | 1.270 | 1.336 | 1.455 | 1.00 | 2500 |
| tau[16,2] | 1.838 | 0.184 | 1.492 | 1.712 | 1.832 | 1.961 | 2.212 | 1.00 | 4000 |
| tau[17,2] | 2.208 | 0.155 | 1.910 | 2.103 | 2.205 | 2.310 | 2.525 | 1.00 | 720 |
| tau[18,2] | 2.091 | 0.147 | 1.801 | 1.993 | 2.090 | 2.188 | 2.388 | 1.00 | 1400 |
| tau[19,2] | 1.954 | 0.100 | 1.766 | 1.884 | 1.953 | 2.021 | 2.153 | 1.00 | 2000 |
| tau[20,2] | 2.643 | 0.153 | 2.352 | 2.537 | 2.640 | 2.745 | 2.953 | 1.00 | 1300 |
| tau[21,2] | 1.650 | 0.103 | 1.450 | 1.581 | 1.648 | 1.719 | 1.854 | 1.00 | 700 |
| tau[22,2] | 2.035 | 0.125 | 1.798 | 1.951 | 2.030 | 2.116 | 2.283 | 1.00 | 700 |
| tau[23,2] | 0.583 | 0.051 | 0.488 | 0.547 | 0.582 | 0.617 | 0.687 | 1.00 | 4000 |
| tau[24,2] | 1.907 | 0.110 | 1.702 | 1.832 | 1.904 | 1.983 | 2.132 | 1.00 | 1000 |
| tau[25,2] | 0.745 | 0.056 | 0.641 | 0.706 | 0.744 | 0.783 | 0.862 | 1.00 | 2300 |
| theta[1] | 2.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 1.00 | 1 |
| theta[2] | 2.553 | 0.272 | 2.069 | 2.357 | 2.536 | 2.729 | 3.135 | 1.00 | 1400 |
| theta[3] | 1.628 | 0.128 | 1.409 | 1.539 | 1.617 | 1.704 | 1.914 | 1.00 | 1500 |
| theta[4] | 1.615 | 0.128 | 1.399 | 1.524 | 1.604 | 1.697 | 1.893 | 1.00 | 1300 |
| theta[5] | 2.003 | 0.185 | 1.691 | 1.873 | 1.989 | 2.116 | 2.405 | 1.00 | 630 |
| theta[6] | 1.847 | 0.157 | 1.572 | 1.736 | 1.836 | 1.946 | 2.198 | 1.00 | 1600 |
| theta[7] | 2.009 | 0.192 | 1.679 | 1.871 | 1.995 | 2.127 | 2.429 | 1.01 | 390 |
| theta[8] | 2.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 1.00 | 1 |
| theta[9] | 1.755 | 0.145 | 1.503 | 1.653 | 1.744 | 1.846 | 2.061 | 1.00 | 1000 |
| theta[10] | 1.597 | 0.117 | 1.395 | 1.514 | 1.589 | 1.670 | 1.852 | 1.00 | 1500 |
| theta[11] | 1.542 | 0.115 | 1.344 | 1.461 | 1.533 | 1.613 | 1.797 | 1.00 | 2900 |
| theta[12] | 2.059 | 0.184 | 1.734 | 1.932 | 2.048 | 2.170 | 2.451 | 1.00 | 4000 |
| theta[13] | 2.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 1.00 | 1 |
| theta[14] | 1.181 | 0.055 | 1.089 | 1.143 | 1.175 | 1.213 | 1.302 | 1.00 | 1300 |
| theta[15] | 1.225 | 0.059 | 1.123 | 1.184 | 1.222 | 1.261 | 1.355 | 1.00 | 1500 |
| theta[16] | 1.089 | 0.037 | 1.031 | 1.063 | 1.085 | 1.111 | 1.173 | 1.00 | 3100 |
| theta[17] | 1.439 | 0.099 | 1.269 | 1.371 | 1.430 | 1.499 | 1.655 | 1.00 | 580 |
| theta[18] | 1.298 | 0.076 | 1.171 | 1.245 | 1.291 | 1.344 | 1.470 | 1.00 | 810 |
| theta[19] | 2.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 2.000 | 1.00 | 1 |
| theta[20] | 2.313 | 0.228 | 1.913 | 2.150 | 2.302 | 2.457 | 2.805 | 1.00 | 1500 |
| theta[21] | 1.941 | 0.166 | 1.648 | 1.821 | 1.931 | 2.044 | 2.301 | 1.00 | 2100 |
| theta[22] | 2.445 | 0.253 | 2.013 | 2.267 | 2.425 | 2.597 | 2.987 | 1.00 | 1300 |
| theta[23] | 1.788 | 0.155 | 1.517 | 1.681 | 1.774 | 1.884 | 2.127 | 1.00 | 1500 |
| theta[24] | 2.012 | 0.175 | 1.705 | 1.889 | 1.999 | 2.125 | 2.383 | 1.00 | 1500 |
| theta[25] | 1.599 | 0.123 | 1.388 | 1.508 | 1.591 | 1.676 | 1.863 | 1.00 | 610 |
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_model1_lambda_dens.pdf")
Saving 7 x 5 in imagebayesplot::mcmc_acf(fit.mcmc, regex_pars = "lambda.std"); ggsave("fig/pools_model1_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_model1_lambda_trace.pdf")
Saving 7 x 5 in imageggmcmc::ggs_grb(fit.mcmc.ggs, family = "lambda.std"); ggsave("fig/pools_model1_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_m1.csv"))Factor Reliability Omega (\(\omega\))
bayesplot::mcmc_areas(fit.mcmc, regex_pars = "reli.omega", prob = 0.8); ggsave("fig/pools_model1_omega_dens.pdf")
Saving 7 x 5 in imagebayesplot::mcmc_acf(fit.mcmc, regex_pars = "reli.omega"); ggsave("fig/pools_model1_omega_acf.pdf")
Saving 7 x 5 in imagebayesplot::mcmc_trace(fit.mcmc, regex_pars = "reli.omega"); ggsave("fig/pools_model1_omega_trace.pdf")
Saving 7 x 5 in imageggmcmc::ggs_grb(fit.mcmc.ggs, family = "reli.omega"); ggsave("fig/pools_model1_omega_grb.pdf")
Saving 7 x 5 in image# extract omega posterior for results comparison
extracted_omega <- data.frame(model_1_f1 = fit.mcmc$`reli.omega[1]`,
model_1_f2 = fit.mcmc$`reli.omega[2]`,
model_1_f3 = fit.mcmc$`reli.omega[3]`,
model_1_f4 = fit.mcmc$`reli.omega[4]`)
write.csv(x=extracted_omega, file=paste0(getwd(),"/data/pools/extracted_omega_m1.csv"))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 04:13:56 2022
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrrrrrr}
\toprule
& mean & sd & 2.5\% & 25\% & 50\% & 75\% & 97.5\% & Rhat & n.eff \\
\midrule
deviance & 15091.74 & 119.05 & 14858.10 & 15012.53 & 15091.66 & 15170.14 & 15327.72 & 1.00 & 4000.00 \\
inv.phi[1,1] & 3.16 & 1.61 & 0.76 & 2.01 & 2.90 & 4.00 & 7.17 & 1.03 & 140.00 \\
inv.phi[2,1] & -0.46 & 1.06 & -2.76 & -1.06 & -0.41 & 0.22 & 1.51 & 1.02 & 160.00 \\
inv.phi[3,1] & -1.25 & 1.10 & -3.85 & -1.85 & -1.12 & -0.46 & 0.49 & 1.01 & 230.00 \\
inv.phi[4,1] & -1.27 & 1.43 & -4.45 & -2.12 & -1.13 & -0.28 & 1.15 & 1.01 & 310.00 \\
inv.phi[1,2] & -0.46 & 1.06 & -2.76 & -1.06 & -0.41 & 0.22 & 1.51 & 1.02 & 160.00 \\
inv.phi[2,2] & 2.87 & 1.46 & 0.89 & 1.78 & 2.59 & 3.68 & 6.31 & 1.01 & 250.00 \\
inv.phi[3,2] & -0.13 & 0.90 & -1.98 & -0.68 & -0.11 & 0.42 & 1.69 & 1.01 & 490.00 \\
inv.phi[4,2] & -1.77 & 1.41 & -4.95 & -2.55 & -1.57 & -0.79 & 0.50 & 1.02 & 180.00 \\
inv.phi[1,3] & -1.25 & 1.10 & -3.85 & -1.85 & -1.12 & -0.46 & 0.49 & 1.01 & 230.00 \\
inv.phi[2,3] & -0.13 & 0.90 & -1.98 & -0.68 & -0.11 & 0.42 & 1.69 & 1.01 & 490.00 \\
inv.phi[3,3] & 2.75 & 1.32 & 0.72 & 1.80 & 2.51 & 3.47 & 5.95 & 1.02 & 180.00 \\
inv.phi[4,3] & -1.00 & 1.14 & -3.50 & -1.67 & -0.89 & -0.23 & 0.91 & 1.01 & 280.00 \\
inv.phi[1,4] & -1.27 & 1.43 & -4.45 & -2.12 & -1.13 & -0.28 & 1.15 & 1.01 & 310.00 \\
inv.phi[2,4] & -1.77 & 1.41 & -4.95 & -2.55 & -1.57 & -0.79 & 0.50 & 1.02 & 180.00 \\
inv.phi[3,4] & -1.00 & 1.14 & -3.50 & -1.67 & -0.89 & -0.23 & 0.91 & 1.01 & 280.00 \\
inv.phi[4,4] & 4.02 & 2.10 & 0.99 & 2.44 & 3.66 & 5.17 & 9.06 & 1.03 & 130.00 \\
lambda[1] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
lambda[2] & 1.24 & 0.11 & 1.03 & 1.16 & 1.24 & 1.31 & 1.46 & 1.00 & 1600.00 \\
lambda[3] & 0.79 & 0.08 & 0.64 & 0.73 & 0.79 & 0.84 & 0.96 & 1.00 & 1500.00 \\
lambda[4] & 0.78 & 0.08 & 0.63 & 0.72 & 0.78 & 0.84 & 0.95 & 1.00 & 1100.00 \\
lambda[5] & 1.00 & 0.09 & 0.83 & 0.93 & 0.99 & 1.06 & 1.19 & 1.00 & 660.00 \\
lambda[6] & 0.92 & 0.09 & 0.76 & 0.86 & 0.91 & 0.97 & 1.09 & 1.00 & 1500.00 \\
lambda[7] & 1.00 & 0.09 & 0.82 & 0.93 & 1.00 & 1.06 & 1.20 & 1.01 & 380.00 \\
lambda[8] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
lambda[9] & 0.86 & 0.08 & 0.71 & 0.81 & 0.86 & 0.92 & 1.03 & 1.00 & 900.00 \\
lambda[10] & 0.77 & 0.08 & 0.63 & 0.72 & 0.77 & 0.82 & 0.92 & 1.00 & 1500.00 \\
lambda[11] & 0.73 & 0.08 & 0.59 & 0.68 & 0.73 & 0.78 & 0.89 & 1.00 & 3000.00 \\
lambda[12] & 1.03 & 0.09 & 0.86 & 0.97 & 1.02 & 1.08 & 1.20 & 1.00 & 3900.00 \\
lambda[13] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
lambda[14] & 0.42 & 0.06 & 0.30 & 0.38 & 0.42 & 0.46 & 0.55 & 1.00 & 1300.00 \\
lambda[15] & 0.47 & 0.06 & 0.35 & 0.43 & 0.47 & 0.51 & 0.60 & 1.00 & 1300.00 \\
lambda[16] & 0.29 & 0.06 & 0.17 & 0.25 & 0.29 & 0.33 & 0.42 & 1.00 & 2100.00 \\
lambda[17] & 0.66 & 0.07 & 0.52 & 0.61 & 0.66 & 0.71 & 0.81 & 1.01 & 560.00 \\
lambda[18] & 0.54 & 0.07 & 0.41 & 0.49 & 0.54 & 0.59 & 0.69 & 1.00 & 840.00 \\
lambda[19] & 1.00 & 0.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\
lambda[20] & 1.14 & 0.10 & 0.96 & 1.07 & 1.14 & 1.21 & 1.34 & 1.00 & 1400.00 \\
lambda[21] & 0.97 & 0.09 & 0.80 & 0.91 & 0.97 & 1.02 & 1.14 & 1.00 & 2400.00 \\
lambda[22] & 1.20 & 0.10 & 1.01 & 1.13 & 1.19 & 1.26 & 1.41 & 1.00 & 1200.00 \\
lambda[23] & 0.88 & 0.09 & 0.72 & 0.83 & 0.88 & 0.94 & 1.06 & 1.00 & 1700.00 \\
lambda[24] & 1.00 & 0.09 & 0.84 & 0.94 & 1.00 & 1.06 & 1.18 & 1.00 & 1600.00 \\
lambda[25] & 0.77 & 0.08 & 0.62 & 0.71 & 0.77 & 0.82 & 0.93 & 1.00 & 640.00 \\
lambda.std[1] & 0.71 & 0.00 & 0.71 & 0.71 & 0.71 & 0.71 & 0.71 & 1.00 & 1.00 \\
lambda.std[2] & 0.78 & 0.03 & 0.72 & 0.76 & 0.78 & 0.80 & 0.83 & 1.00 & 1900.00 \\
lambda.std[3] & 0.62 & 0.04 & 0.54 & 0.59 & 0.62 & 0.64 & 0.69 & 1.00 & 1500.00 \\
lambda.std[4] & 0.61 & 0.04 & 0.53 & 0.59 & 0.61 & 0.64 & 0.69 & 1.00 & 1100.00 \\
lambda.std[5] & 0.70 & 0.03 & 0.64 & 0.68 & 0.71 & 0.73 & 0.76 & 1.00 & 690.00 \\
lambda.std[6] & 0.67 & 0.03 & 0.60 & 0.65 & 0.67 & 0.70 & 0.74 & 1.00 & 1400.00 \\
lambda.std[7] & 0.70 & 0.03 & 0.64 & 0.68 & 0.71 & 0.73 & 0.77 & 1.01 & 370.00 \\
lambda.std[8] & 0.71 & 0.00 & 0.71 & 0.71 & 0.71 & 0.71 & 0.71 & 1.00 & 1.00 \\
lambda.std[9] & 0.65 & 0.04 & 0.58 & 0.63 & 0.65 & 0.68 & 0.72 & 1.00 & 830.00 \\
lambda.std[10] & 0.61 & 0.04 & 0.53 & 0.58 & 0.61 & 0.63 & 0.68 & 1.00 & 1500.00 \\
lambda.std[11] & 0.59 & 0.04 & 0.51 & 0.56 & 0.59 & 0.62 & 0.67 & 1.00 & 3100.00 \\
lambda.std[12] & 0.71 & 0.03 & 0.65 & 0.69 & 0.72 & 0.73 & 0.77 & 1.00 & 3600.00 \\
lambda.std[13] & 0.71 & 0.00 & 0.71 & 0.71 & 0.71 & 0.71 & 0.71 & 1.00 & 1.00 \\
lambda.std[14] & 0.39 & 0.05 & 0.29 & 0.35 & 0.39 & 0.42 & 0.48 & 1.00 & 1400.00 \\
lambda.std[15] & 0.42 & 0.05 & 0.33 & 0.39 & 0.43 & 0.45 & 0.51 & 1.00 & 1300.00 \\
lambda.std[16] & 0.28 & 0.05 & 0.17 & 0.24 & 0.28 & 0.32 & 0.38 & 1.00 & 2100.00 \\
lambda.std[17] & 0.55 & 0.04 & 0.46 & 0.52 & 0.55 & 0.58 & 0.63 & 1.01 & 550.00 \\
lambda.std[18] & 0.47 & 0.05 & 0.38 & 0.44 & 0.47 & 0.51 & 0.57 & 1.00 & 860.00 \\
lambda.std[19] & 0.71 & 0.00 & 0.71 & 0.71 & 0.71 & 0.71 & 0.71 & 1.00 & 1.00 \\
lambda.std[20] & 0.75 & 0.03 & 0.69 & 0.73 & 0.75 & 0.77 & 0.80 & 1.00 & 1300.00 \\
lambda.std[21] & 0.69 & 0.03 & 0.63 & 0.67 & 0.69 & 0.71 & 0.75 & 1.00 & 2800.00 \\
lambda.std[22] & 0.77 & 0.03 & 0.71 & 0.75 & 0.77 & 0.78 & 0.82 & 1.00 & 1100.00 \\
lambda.std[23] & 0.66 & 0.04 & 0.58 & 0.64 & 0.66 & 0.68 & 0.73 & 1.00 & 1800.00 \\
lambda.std[24] & 0.71 & 0.03 & 0.64 & 0.69 & 0.71 & 0.73 & 0.76 & 1.00 & 1700.00 \\
lambda.std[25] & 0.61 & 0.04 & 0.53 & 0.58 & 0.61 & 0.64 & 0.68 & 1.00 & 660.00 \\
phi[1,1] & 2.62 & 1.63 & 0.86 & 1.64 & 2.25 & 3.05 & 7.22 & 1.03 & 170.00 \\
phi[2,1] & 1.58 & 0.88 & 0.19 & 1.01 & 1.51 & 2.04 & 3.64 & 1.03 & 1800.00 \\
phi[3,1] & 1.93 & 1.05 & 0.46 & 1.23 & 1.77 & 2.38 & 4.55 & 1.02 & 490.00 \\
phi[4,1] & 1.91 & 0.74 & 0.53 & 1.46 & 1.90 & 2.32 & 3.47 & 1.03 & 360.00 \\
phi[1,2] & 1.58 & 0.88 & 0.19 & 1.01 & 1.51 & 2.04 & 3.64 & 1.03 & 1800.00 \\
phi[2,2] & 2.08 & 1.02 & 0.72 & 1.37 & 1.90 & 2.52 & 4.72 & 1.02 & 280.00 \\
phi[3,2] & 1.40 & 0.82 & 0.03 & 0.84 & 1.31 & 1.88 & 3.14 & 1.02 & 200.00 \\
phi[4,2] & 1.68 & 0.70 & 0.19 & 1.26 & 1.71 & 2.12 & 3.06 & 1.02 & 270.00 \\
phi[1,3] & 1.93 & 1.05 & 0.46 & 1.23 & 1.77 & 2.38 & 4.55 & 1.02 & 490.00 \\
phi[2,3] & 1.40 & 0.82 & 0.03 & 0.84 & 1.31 & 1.88 & 3.14 & 1.02 & 200.00 \\
phi[3,3] & 2.37 & 1.20 & 0.82 & 1.52 & 2.13 & 2.95 & 5.18 & 1.00 & 4000.00 \\
phi[4,3] & 1.78 & 0.67 & 0.43 & 1.34 & 1.78 & 2.22 & 3.07 & 1.01 & 550.00 \\
phi[1,4] & 1.91 & 0.74 & 0.53 & 1.46 & 1.90 & 2.32 & 3.47 & 1.03 & 360.00 \\
phi[2,4] & 1.68 & 0.70 & 0.19 & 1.26 & 1.71 & 2.12 & 3.06 & 1.02 & 270.00 \\
phi[3,4] & 1.78 & 0.67 & 0.43 & 1.34 & 1.78 & 2.22 & 3.07 & 1.01 & 550.00 \\
phi[4,4] & 2.29 & 0.24 & 1.86 & 2.12 & 2.28 & 2.44 & 2.81 & 1.00 & 1800.00 \\
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] & 0.70 & 0.21 & 0.12 & 0.62 & 0.75 & 0.84 & 0.93 & 1.03 & 200.00 \\
phi.cor[3,1] & 0.78 & 0.16 & 0.37 & 0.72 & 0.83 & 0.89 & 0.95 & 1.01 & 780.00 \\
phi.cor[4,1] & 0.80 & 0.17 & 0.32 & 0.75 & 0.86 & 0.91 & 0.95 & 1.03 & 890.00 \\
phi.cor[1,2] & 0.70 & 0.21 & 0.12 & 0.62 & 0.75 & 0.84 & 0.93 & 1.03 & 200.00 \\
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] & 0.64 & 0.24 & 0.02 & 0.53 & 0.71 & 0.82 & 0.91 & 1.02 & 170.00 \\
phi.cor[4,2] & 0.77 & 0.22 & 0.11 & 0.72 & 0.85 & 0.90 & 0.95 & 1.05 & 210.00 \\
phi.cor[1,3] & 0.78 & 0.16 & 0.37 & 0.72 & 0.83 & 0.89 & 0.95 & 1.01 & 780.00 \\
phi.cor[2,3] & 0.64 & 0.24 & 0.02 & 0.53 & 0.71 & 0.82 & 0.91 & 1.02 & 170.00 \\
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] & 0.77 & 0.18 & 0.24 & 0.72 & 0.83 & 0.89 & 0.94 & 1.02 & 200.00 \\
phi.cor[1,4] & 0.80 & 0.17 & 0.32 & 0.75 & 0.86 & 0.91 & 0.95 & 1.03 & 890.00 \\
phi.cor[2,4] & 0.77 & 0.22 & 0.11 & 0.72 & 0.85 & 0.90 & 0.95 & 1.05 & 210.00 \\
phi.cor[3,4] & 0.77 & 0.18 & 0.24 & 0.72 & 0.83 & 0.89 & 0.94 & 1.02 & 200.00 \\
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] & 0.87 & 0.01 & 0.84 & 0.86 & 0.87 & 0.87 & 0.89 & 1.01 & 430.00 \\
reli.omega[2] & 0.83 & 0.01 & 0.81 & 0.82 & 0.83 & 0.84 & 0.85 & 1.00 & 4000.00 \\
reli.omega[3] & 0.65 & 0.03 & 0.60 & 0.64 & 0.66 & 0.67 & 0.70 & 1.01 & 410.00 \\
reli.omega[4] & 0.86 & 0.01 & 0.84 & 0.86 & 0.86 & 0.87 & 0.88 & 1.00 & 3100.00 \\
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] & 2.84 & 0.15 & 2.55 & 2.74 & 2.84 & 2.94 & 3.14 & 1.00 & 3900.00 \\
tau[2,2] & 3.23 & 0.20 & 2.87 & 3.10 & 3.23 & 3.37 & 3.65 & 1.00 & 2300.00 \\
tau[3,2] & 2.35 & 0.15 & 2.06 & 2.25 & 2.35 & 2.45 & 2.65 & 1.00 & 1800.00 \\
tau[4,2] & 1.78 & 0.12 & 1.56 & 1.70 & 1.78 & 1.86 & 2.01 & 1.00 & 770.00 \\
tau[5,2] & 2.30 & 0.14 & 2.02 & 2.19 & 2.30 & 2.40 & 2.58 & 1.00 & 840.00 \\
tau[6,2] & 2.59 & 0.16 & 2.30 & 2.47 & 2.58 & 2.69 & 2.91 & 1.00 & 2700.00 \\
tau[7,2] & 3.09 & 0.20 & 2.71 & 2.95 & 3.08 & 3.22 & 3.48 & 1.00 & 1500.00 \\
tau[8,2] & 1.90 & 0.11 & 1.69 & 1.83 & 1.90 & 1.97 & 2.11 & 1.00 & 4000.00 \\
tau[9,2] & 1.10 & 0.08 & 0.94 & 1.04 & 1.09 & 1.15 & 1.26 & 1.00 & 1200.00 \\
tau[10,2] & 1.44 & 0.10 & 1.25 & 1.37 & 1.44 & 1.50 & 1.64 & 1.00 & 4000.00 \\
tau[11,2] & 0.67 & 0.05 & 0.56 & 0.63 & 0.66 & 0.70 & 0.78 & 1.00 & 3100.00 \\
tau[12,2] & 1.70 & 0.10 & 1.50 & 1.63 & 1.70 & 1.76 & 1.90 & 1.00 & 3300.00 \\
tau[13,2] & 1.68 & 0.09 & 1.50 & 1.61 & 1.68 & 1.74 & 1.86 & 1.00 & 1500.00 \\
tau[14,2] & 1.64 & 0.13 & 1.38 & 1.55 & 1.64 & 1.72 & 1.90 & 1.00 & 1000.00 \\
tau[15,2] & 1.27 & 0.09 & 1.10 & 1.21 & 1.27 & 1.34 & 1.46 & 1.00 & 2500.00 \\
tau[16,2] & 1.84 & 0.18 & 1.49 & 1.71 & 1.83 & 1.96 & 2.21 & 1.00 & 4000.00 \\
tau[17,2] & 2.21 & 0.16 & 1.91 & 2.10 & 2.21 & 2.31 & 2.52 & 1.00 & 720.00 \\
tau[18,2] & 2.09 & 0.15 & 1.80 & 1.99 & 2.09 & 2.19 & 2.39 & 1.00 & 1400.00 \\
tau[19,2] & 1.95 & 0.10 & 1.77 & 1.88 & 1.95 & 2.02 & 2.15 & 1.00 & 2000.00 \\
tau[20,2] & 2.64 & 0.15 & 2.35 & 2.54 & 2.64 & 2.75 & 2.95 & 1.00 & 1300.00 \\
tau[21,2] & 1.65 & 0.10 & 1.45 & 1.58 & 1.65 & 1.72 & 1.85 & 1.00 & 700.00 \\
tau[22,2] & 2.03 & 0.12 & 1.80 & 1.95 & 2.03 & 2.12 & 2.28 & 1.00 & 700.00 \\
tau[23,2] & 0.58 & 0.05 & 0.49 & 0.55 & 0.58 & 0.62 & 0.69 & 1.00 & 4000.00 \\
tau[24,2] & 1.91 & 0.11 & 1.70 & 1.83 & 1.90 & 1.98 & 2.13 & 1.00 & 1000.00 \\
tau[25,2] & 0.75 & 0.06 & 0.64 & 0.71 & 0.74 & 0.78 & 0.86 & 1.00 & 2300.00 \\
theta[1] & 2.00 & 0.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 1.00 & 1.00 \\
theta[2] & 2.55 & 0.27 & 2.07 & 2.36 & 2.54 & 2.73 & 3.13 & 1.00 & 1400.00 \\
theta[3] & 1.63 & 0.13 & 1.41 & 1.54 & 1.62 & 1.70 & 1.91 & 1.00 & 1500.00 \\
theta[4] & 1.62 & 0.13 & 1.40 & 1.52 & 1.60 & 1.70 & 1.89 & 1.00 & 1300.00 \\
theta[5] & 2.00 & 0.19 & 1.69 & 1.87 & 1.99 & 2.12 & 2.40 & 1.00 & 630.00 \\
theta[6] & 1.85 & 0.16 & 1.57 & 1.74 & 1.84 & 1.95 & 2.20 & 1.00 & 1600.00 \\
theta[7] & 2.01 & 0.19 & 1.68 & 1.87 & 1.99 & 2.13 & 2.43 & 1.01 & 390.00 \\
theta[8] & 2.00 & 0.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 1.00 & 1.00 \\
theta[9] & 1.75 & 0.14 & 1.50 & 1.65 & 1.74 & 1.85 & 2.06 & 1.00 & 1000.00 \\
theta[10] & 1.60 & 0.12 & 1.40 & 1.51 & 1.59 & 1.67 & 1.85 & 1.00 & 1500.00 \\
theta[11] & 1.54 & 0.12 & 1.34 & 1.46 & 1.53 & 1.61 & 1.80 & 1.00 & 2900.00 \\
theta[12] & 2.06 & 0.18 & 1.73 & 1.93 & 2.05 & 2.17 & 2.45 & 1.00 & 4000.00 \\
theta[13] & 2.00 & 0.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 1.00 & 1.00 \\
theta[14] & 1.18 & 0.05 & 1.09 & 1.14 & 1.18 & 1.21 & 1.30 & 1.00 & 1300.00 \\
theta[15] & 1.23 & 0.06 & 1.12 & 1.18 & 1.22 & 1.26 & 1.36 & 1.00 & 1500.00 \\
theta[16] & 1.09 & 0.04 & 1.03 & 1.06 & 1.09 & 1.11 & 1.17 & 1.00 & 3100.00 \\
theta[17] & 1.44 & 0.10 & 1.27 & 1.37 & 1.43 & 1.50 & 1.66 & 1.00 & 580.00 \\
theta[18] & 1.30 & 0.08 & 1.17 & 1.24 & 1.29 & 1.34 & 1.47 & 1.00 & 810.00 \\
theta[19] & 2.00 & 0.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 1.00 & 1.00 \\
theta[20] & 2.31 & 0.23 & 1.91 & 2.15 & 2.30 & 2.46 & 2.80 & 1.00 & 1500.00 \\
theta[21] & 1.94 & 0.17 & 1.65 & 1.82 & 1.93 & 2.04 & 2.30 & 1.00 & 2100.00 \\
theta[22] & 2.44 & 0.25 & 2.01 & 2.27 & 2.42 & 2.60 & 2.99 & 1.00 & 1300.00 \\
theta[23] & 1.79 & 0.16 & 1.52 & 1.68 & 1.77 & 1.88 & 2.13 & 1.00 & 1500.00 \\
theta[24] & 2.01 & 0.17 & 1.70 & 1.89 & 2.00 & 2.12 & 2.38 & 1.00 & 1500.00 \\
theta[25] & 1.60 & 0.12 & 1.39 & 1.51 & 1.59 & 1.68 & 1.86 & 1.00 & 610.00 \\
\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
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