SERA POOLS Study
Comparison of Reliability Posterior Distribution
R. Noah Padgett
2022-02-01
Last updated: 2022-02-02
Checks: 5 1
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)Model Comparison
DWLS
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 0.03t(apply(mydata,2, table)) 1 2 3
Q4_3 232 211 47
Q4_4 222 220 48
Q4_5 285 154 51
Q4_9 258 147 85
Q4_11 247 171 72
Q4_15 258 180 52
Q4_18 272 181 37
Q5_1 229 164 97
Q5_2 180 131 179
Q5_3 225 145 120
Q5_5 108 113 269
Q5_6 161 195 134
Q5_12 170 188 132
Q6_2 346 94 50
Q6_5 266 123 101
Q6_6 405 61 24
Q6_7 316 129 45
Q6_8 321 127 42
Q6_11 183 199 108
Q7_2 197 224 69
Q7_4 183 180 127
Q7_5 171 203 116
Q7_7 89 101 300
Q7_8 174 205 111
Q7_14 82 135 273mod <- '
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)
summary(fit.dwls, standardized=T, fit.measures=T)lavaan 0.6-7 ended normally after 49 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 0.777 0.777
Q4_4 (lm44) 1.123 0.037 30.348 0.000 0.872 0.872
Q4_5 (lm45) 0.979 0.043 22.805 0.000 0.760 0.760
Q4_9 (lm49) 0.883 0.047 18.643 0.000 0.686 0.686
Q4_11 (l411) 1.018 0.041 24.844 0.000 0.791 0.791
Q4_15 (l415) 0.998 0.043 23.381 0.000 0.775 0.775
Q4_18 (l418) 1.086 0.040 27.368 0.000 0.843 0.843
SC =~
Q5_1 1.000 0.734 0.734
Q5_2 (lm52) 0.989 0.056 17.522 0.000 0.726 0.726
Q5_3 (lm53) 0.973 0.061 15.972 0.000 0.714 0.714
Q5_5 (lm55) 0.893 0.066 13.477 0.000 0.656 0.656
Q5_6 (lm56) 1.087 0.060 18.085 0.000 0.798 0.798
Q5_12 (l512) 1.075 0.061 17.704 0.000 0.789 0.789
IN =~
Q6_2 1.000 0.725 0.725
Q6_5 (lm65) 0.752 0.071 10.620 0.000 0.546 0.546
Q6_6 (lm66) 1.204 0.068 17.697 0.000 0.874 0.874
Q6_7 (lm67) 1.169 0.066 17.758 0.000 0.848 0.848
Q6_8 (lm68) 1.093 0.060 18.255 0.000 0.793 0.793
Q6_11 (l611) 1.186 0.070 16.932 0.000 0.860 0.860
EN =~
Q7_2 1.000 0.779 0.779
Q7_4 (lm74) 0.905 0.045 20.233 0.000 0.705 0.705
Q7_5 (lm75) 1.039 0.046 22.632 0.000 0.809 0.809
Q7_7 (lm77) 0.945 0.059 16.113 0.000 0.736 0.736
Q7_8 (lm78) 0.941 0.044 21.465 0.000 0.733 0.733
Q7_14 (l714) 0.823 0.056 14.725 0.000 0.641 0.641
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL ~~
SC 0.391 0.032 12.101 0.000 0.685 0.685
IN 0.422 0.035 12.131 0.000 0.748 0.748
EN 0.471 0.030 15.840 0.000 0.778 0.778
SC ~~
IN 0.346 0.035 9.904 0.000 0.649 0.649
EN 0.460 0.033 13.989 0.000 0.803 0.803
IN ~~
EN 0.422 0.036 11.883 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 -0.067 0.057 -1.173 0.241 -0.067 -0.067
Q4_3|t2 1.305 0.078 16.683 0.000 1.305 1.305
Q4_4|t1 -0.118 0.057 -2.076 0.038 -0.118 -0.118
Q4_4|t2 1.293 0.078 16.631 0.000 1.293 1.293
Q4_5|t1 0.206 0.057 3.608 0.000 0.206 0.206
Q4_5|t2 1.259 0.076 16.468 0.000 1.259 1.259
Q4_9|t1 0.067 0.057 1.173 0.241 0.067 0.067
Q4_9|t2 0.941 0.067 14.080 0.000 0.941 0.941
Q4_11|t1 0.010 0.057 0.181 0.857 0.010 0.010
Q4_11|t2 1.050 0.070 15.077 0.000 1.050 1.050
Q4_15|t1 0.067 0.057 1.173 0.241 0.067 0.067
Q4_15|t2 1.247 0.076 16.411 0.000 1.247 1.247
Q4_18|t1 0.139 0.057 2.436 0.015 0.139 0.139
Q4_18|t2 1.436 0.084 17.100 0.000 1.436 1.436
Q5_1|t1 -0.082 0.057 -1.444 0.149 -0.082 -0.082
Q5_1|t2 0.849 0.065 13.109 0.000 0.849 0.849
Q5_2|t1 -0.339 0.058 -5.855 0.000 -0.339 -0.339
Q5_2|t2 0.344 0.058 5.945 0.000 0.344 0.344
Q5_3|t1 -0.102 0.057 -1.805 0.071 -0.102 -0.102
Q5_3|t2 0.691 0.062 11.162 0.000 0.691 0.691
Q5_5|t1 -0.771 0.063 -12.188 0.000 -0.771 -0.771
Q5_5|t2 -0.123 0.057 -2.166 0.030 -0.123 -0.123
Q5_6|t1 -0.444 0.059 -7.555 0.000 -0.444 -0.444
Q5_6|t2 0.602 0.061 9.944 0.000 0.602 0.602
Q5_12|t1 -0.394 0.058 -6.751 0.000 -0.394 -0.394
Q5_12|t2 0.615 0.061 10.119 0.000 0.615 0.615
Q6_2|t1 0.542 0.060 9.064 0.000 0.542 0.542
Q6_2|t2 1.270 0.077 16.524 0.000 1.270 1.270
Q6_5|t1 0.108 0.057 1.895 0.058 0.108 0.108
Q6_5|t2 0.820 0.064 12.777 0.000 0.820 0.820
Q6_6|t1 0.941 0.067 14.080 0.000 0.941 0.941
Q6_6|t2 1.655 0.096 17.201 0.000 1.655 1.655
Q6_7|t1 0.372 0.058 6.393 0.000 0.372 0.372
Q6_7|t2 1.330 0.079 16.782 0.000 1.330 1.330
Q6_8|t1 0.399 0.058 6.841 0.000 0.399 0.399
Q6_8|t2 1.368 0.081 16.917 0.000 1.368 1.368
Q6_11|t1 -0.323 0.058 -5.586 0.000 -0.323 -0.323
Q6_11|t2 0.771 0.063 12.188 0.000 0.771 0.771
Q7_2|t1 -0.248 0.057 -4.328 0.000 -0.248 -0.248
Q7_2|t2 1.077 0.070 15.295 0.000 1.077 1.077
Q7_4|t1 -0.323 0.058 -5.586 0.000 -0.323 -0.323
Q7_4|t2 0.646 0.061 10.555 0.000 0.646 0.646
Q7_5|t1 -0.388 0.058 -6.662 0.000 -0.388 -0.388
Q7_5|t2 0.717 0.062 11.506 0.000 0.717 0.717
Q7_7|t1 -0.909 0.066 -13.761 0.000 -0.909 -0.909
Q7_7|t2 -0.285 0.058 -4.958 0.000 -0.285 -0.285
Q7_8|t1 -0.372 0.058 -6.393 0.000 -0.372 -0.372
Q7_8|t2 0.750 0.063 11.934 0.000 0.750 0.750
Q7_14|t1 -0.965 0.067 -14.316 0.000 -0.965 -0.965
Q7_14|t2 -0.144 0.057 -2.526 0.012 -0.144 -0.144
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL 0.604 0.037 16.186 0.000 1.000 1.000
SC 0.539 0.049 11.113 0.000 1.000 1.000
IN 0.526 0.052 10.033 0.000 1.000 1.000
EN 0.607 0.042 14.304 0.000 1.000 1.000
.Q4_3 0.396 0.396 0.396
.Q4_4 0.239 0.239 0.239
.Q4_5 0.422 0.422 0.422
.Q4_9 0.530 0.530 0.530
.Q4_11 0.374 0.374 0.374
.Q4_15 0.399 0.399 0.399
.Q4_18 0.289 0.289 0.289
.Q5_1 0.461 0.461 0.461
.Q5_2 0.473 0.473 0.473
.Q5_3 0.490 0.490 0.490
.Q5_5 0.570 0.570 0.570
.Q5_6 0.363 0.363 0.363
.Q5_12 0.377 0.377 0.377
.Q6_2 0.474 0.474 0.474
.Q6_5 0.702 0.702 0.702
.Q6_6 0.237 0.237 0.237
.Q6_7 0.281 0.281 0.281
.Q6_8 0.371 0.371 0.371
.Q6_11 0.260 0.260 0.260
.Q7_2 0.393 0.393 0.393
.Q7_4 0.503 0.503 0.503
.Q7_5 0.345 0.345 0.345
.Q7_7 0.458 0.458 0.458
.Q7_8 0.463 0.463 0.463
.Q7_14 0.589 0.589 0.589
Scales y*:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Q4_3 1.000 1.000 1.000
Q4_4 1.000 1.000 1.000
Q4_5 1.000 1.000 1.000
Q4_9 1.000 1.000 1.000
Q4_11 1.000 1.000 1.000
Q4_15 1.000 1.000 1.000
Q4_18 1.000 1.000 1.000
Q5_1 1.000 1.000 1.000
Q5_2 1.000 1.000 1.000
Q5_3 1.000 1.000 1.000
Q5_5 1.000 1.000 1.000
Q5_6 1.000 1.000 1.000
Q5_12 1.000 1.000 1.000
Q6_2 1.000 1.000 1.000
Q6_5 1.000 1.000 1.000
Q6_6 1.000 1.000 1.000
Q6_7 1.000 1.000 1.000
Q6_8 1.000 1.000 1.000
Q6_11 1.000 1.000 1.000
Q7_2 1.000 1.000 1.000
Q7_4 1.000 1.000 1.000
Q7_5 1.000 1.000 1.000
Q7_7 1.000 1.000 1.000
Q7_8 1.000 1.000 1.000
Q7_14 1.000 1.000 1.000
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
rEL 0.878 0.005 160.558 0.000 0.824 0.824
rSC 0.858 0.010 89.363 0.000 0.785 0.785
rIN 0.872 0.009 93.290 0.000 0.801 0.801
rEN 0.842 0.008 101.139 0.000 0.781 0.781Comparing Reliability
# simulated induced prior on omega
prior_lambda <- function(){
y <- -1
while(y < 0){
y <- rnorm(1, 0, 1)
}
return(y)
}
prior_omega <- function(lambda, theta){
(sum(lambda)**2)/(sum(lambda)**2 + sum(theta))
}
nsim=4000
sim_omega1 <- sim_omega2 <- numeric(nsim)
for(i in 1:nsim){
lam_vec <- c(
1, prior_lambda(), prior_lambda(), prior_lambda(),prior_lambda(), prior_lambda(), prior_lambda()
)
tht_vec <- rep(1, 7)
sim_omega1[i] <- prior_omega(lam_vec, tht_vec)
sim_omega2[i] <- prior_omega(lam_vec[1:6], tht_vec[1:6])
}
prior_data <- data.frame(prior_s_f1=sim_omega1,prior_s_f2=sim_omega2,prior_s_f3=sim_omega2,prior_s_f4=sim_omega2)
ggplot(prior_data, aes(x=prior_s_f1))+
geom_density(adjust=2)+
labs(title="Induced prior on omega")+
theme_classic()
# read in data
o1 <- readr::read_csv(paste0(getwd(),"/data/pools/extracted_omega_m1.csv"))Warning: Missing column names filled in: 'X1' [1]
-- Column specification --------------------------------------------------------
cols(
X1 = col_double(),
model_1_f1 = col_double(),
model_1_f2 = col_double(),
model_1_f3 = col_double(),
model_1_f4 = col_double()
)o2 <- readr::read_csv(paste0(getwd(),"/data/pools/extracted_omega_m2.csv"))Warning: Missing column names filled in: 'X1' [1]
-- Column specification --------------------------------------------------------
cols(
X1 = col_double(),
model_2_f1 = col_double(),
model_2_f2 = col_double(),
model_2_f3 = col_double(),
model_2_f4 = col_double()
)o3 <- readr::read_csv(paste0(getwd(),"/data/pools/extracted_omega_m3.csv"))Warning: Missing column names filled in: 'X1' [1]
-- Column specification --------------------------------------------------------
cols(
X1 = col_double(),
model_3_f1 = col_double(),
model_3_f2 = col_double(),
model_3_f3 = col_double(),
model_3_f4 = col_double()
)o4 <- readr::read_csv(paste0(getwd(),"/data/pools/extracted_omega_m4.csv"))Warning: Missing column names filled in: 'X1' [1]
-- Column specification --------------------------------------------------------
cols(
X1 = col_double(),
model_4_f1 = col_double(),
model_4_f2 = col_double(),
model_4_f3 = col_double(),
model_4_f4 = col_double()
)dat_omega <- cbind(o1[,2:5], o2[,2:5], o3[,2:5], o4[,2:5], prior_data[,1:4])
plot.dat <- dat_omega %>%
pivot_longer(
cols=everything(),
names_to = "model",
values_to = "est"
)%>%
mutate(
factor = substr(model, 9,10),
model = substr(model, 1, 7)
)
sum.dat <- plot.dat %>%
group_by(model, factor) %>%
summarise(
Mean = mean(est),
SD = sd(est),
Q025 = quantile(est, 0.025),
Q1 = quantile(est, 0.25),
Median = median(est),
Q3 = quantile(est, 0.75),
Q975 = quantile(est, 0.975),
)`summarise()` has grouped output by 'model'. You can override using the `.groups` argument.kable(sum.dat,format = "html", digits=3) %>%
kable_styling(full_width = T)| model | factor | Mean | SD | Q025 | Q1 | Median | Q3 | Q975 |
|---|---|---|---|---|---|---|---|---|
| model_1 | f1 | 0.865 | 0.011 | 0.843 | 0.858 | 0.866 | 0.873 | 0.886 |
| model_1 | f2 | 0.828 | 0.011 | 0.807 | 0.821 | 0.829 | 0.836 | 0.849 |
| model_1 | f3 | 0.655 | 0.025 | 0.604 | 0.638 | 0.655 | 0.672 | 0.703 |
| model_1 | f4 | 0.864 | 0.011 | 0.842 | 0.857 | 0.864 | 0.872 | 0.884 |
| model_2 | f1 | 0.917 | 0.016 | 0.882 | 0.909 | 0.918 | 0.927 | 0.943 |
| model_2 | f2 | 0.796 | 0.018 | 0.756 | 0.784 | 0.797 | 0.809 | 0.826 |
| model_2 | f3 | 0.872 | 0.021 | 0.827 | 0.857 | 0.873 | 0.888 | 0.906 |
| model_2 | f4 | 0.841 | 0.018 | 0.799 | 0.830 | 0.842 | 0.853 | 0.873 |
| model_3 | f1 | 0.910 | 0.009 | 0.891 | 0.904 | 0.911 | 0.917 | 0.927 |
| model_3 | f2 | 0.824 | 0.012 | 0.799 | 0.815 | 0.824 | 0.833 | 0.846 |
| model_3 | f3 | 0.852 | 0.020 | 0.812 | 0.838 | 0.853 | 0.866 | 0.888 |
| model_3 | f4 | 0.861 | 0.012 | 0.837 | 0.853 | 0.861 | 0.869 | 0.884 |
| model_4 | f1 | 0.848 | 0.015 | 0.817 | 0.838 | 0.848 | 0.858 | 0.878 |
| model_4 | f2 | 0.857 | 0.013 | 0.832 | 0.847 | 0.857 | 0.866 | 0.883 |
| model_4 | f3 | 0.680 | 0.024 | 0.634 | 0.663 | 0.680 | 0.696 | 0.727 |
| model_4 | f4 | 0.903 | 0.011 | 0.881 | 0.896 | 0.904 | 0.911 | 0.923 |
| prior_s | f1 | 0.806 | 0.084 | 0.596 | 0.763 | 0.823 | 0.866 | 0.921 |
| prior_s | f2 | 0.781 | 0.095 | 0.537 | 0.732 | 0.800 | 0.850 | 0.910 |
| prior_s | f3 | 0.781 | 0.095 | 0.537 | 0.732 | 0.800 | 0.850 | 0.910 |
| prior_s | f4 | 0.781 | 0.095 | 0.537 | 0.732 | 0.800 | 0.850 | 0.910 |
# reli from DWLS
reli_est <- data.frame(
factor = c('f1', 'f2', 'f3', 'f4'),
est = c(.82, .79, .80, .78)
)
plot.dat %>%
filter(model != 'prior_s')%>%
ggplot(aes(x=est, y=model, group=interaction(model, factor)))+
ggdist::stat_halfeye(
adjust=2, justification=-0.1,.width=0, point_colour=NA,
normalize="all", fill="grey75"
) +
geom_boxplot(
width=.15, outlier.color = NA, alpha=0.5
) +
geom_vline(data=reli_est, aes(xintercept=est), linetype='dashed')+
labs(x="Reliability Estimates",
y="Estimating Model")+
lims(x=c(0.5, 1))+
facet_wrap(.~factor)+
theme_classic()
ggplot(plot.dat,aes(x=est, group=interaction(model, factor), color=model, fill=model))+
geom_density(alpha=0.25)+
facet_wrap(.~factor)+
theme_classic()
Comparing Factor Correlations
prior_cor <- function(sigma){
S <- LaplacesDemon::rinvwishart(4, matrix(
sigma, ncol=4, nrow=4, byrow=T
))
cor <- matrix(nrow=4,ncol=4)
for(i in 1:4){
for(j in 1:4){
cor[i,j] <- S[i,j]/(sqrt(S[i,i])*sqrt(S[j,j]))
}
}
return(c(cor[1,2],cor[1,3],cor[1,4],cor[2,3],cor[2,4],cor[3,4]))
}
# simulated induced prior on omega
nsim=4000
sim_cor1 <- sim_cor2 <- sim_cor3 <- matrix(nrow=nsim, ncol=6)
for(i in 1:nsim){
sim_cor1[i,] <- prior_cor(c(diag(ncol=4,nrow=4)))
sim_cor2[i,] <- prior_cor(
c(
1, 0.3, 0.3, 0.3,
0.3, 1, 0.3, 0.3,
0.3, 0.3, 1, 0.3,
0.3, 0.3, 0.3, 1)
)
sim_cor3[i,] <- prior_cor(
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)
)
}
prior_data1 <- as.data.frame(sim_cor1)
prior_data2 <- as.data.frame(sim_cor2)
prior_data3 <- as.data.frame(sim_cor3)
colnames(prior_data1) <- 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]')
colnames(prior_data2) <- 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]')
colnames(prior_data3) <- 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]')
prior_data1$model <- 'prior'
prior_data2$model <- 'prior'
prior_data3$model <- 'prior'
prior_data1 %>%
pivot_longer(
cols=contains('phi.cor'),
names_to = 'cor',
values_to="cor_est"
) %>%
ggplot(aes(x=cor_est))+
geom_density(adjust=2)+
facet_wrap(.~cor)+
labs(title="Induced prior on correlations")+
theme_classic()
# read in data
o1 <- readr::read_csv(paste0(getwd(),"/data/pools/extracted_cor_m1.csv")); o1$model <- 'model_1'Warning: Missing column names filled in: 'X1' [1]
-- Column specification --------------------------------------------------------
cols(
X1 = col_double(),
`phi.cor[1,2]` = col_double(),
`phi.cor[1,3]` = col_double(),
`phi.cor[1,4]` = col_double(),
`phi.cor[2,3]` = col_double(),
`phi.cor[2,4]` = col_double(),
`phi.cor[3,4]` = col_double()
)o2 <- readr::read_csv(paste0(getwd(),"/data/pools/extracted_cor_m2.csv")); o2$model <- 'model_2'Warning: Missing column names filled in: 'X1' [1]
-- Column specification --------------------------------------------------------
cols(
X1 = col_double(),
`phi.cor[1,2]` = col_double(),
`phi.cor[1,3]` = col_double(),
`phi.cor[1,4]` = col_double(),
`phi.cor[2,3]` = col_double(),
`phi.cor[2,4]` = col_double(),
`phi.cor[3,4]` = col_double()
)o3 <- readr::read_csv(paste0(getwd(),"/data/pools/extracted_cor_m3.csv")); o3$model <- 'model_3'Warning: Missing column names filled in: 'X1' [1]
-- Column specification --------------------------------------------------------
cols(
X1 = col_double(),
`phi.cor[1,2]` = col_double(),
`phi.cor[1,3]` = col_double(),
`phi.cor[1,4]` = col_double(),
`phi.cor[2,3]` = col_double(),
`phi.cor[2,4]` = col_double(),
`phi.cor[3,4]` = col_double()
)o4 <- readr::read_csv(paste0(getwd(),"/data/pools/extracted_cor_m4.csv")); o4$model <- 'model_4'Warning: Missing column names filled in: 'X1' [1]
-- Column specification --------------------------------------------------------
cols(
X1 = col_double(),
`phi.cor[1,2]` = col_double(),
`phi.cor[1,3]` = col_double(),
`phi.cor[1,4]` = col_double(),
`phi.cor[2,3]` = col_double(),
`phi.cor[2,4]` = col_double(),
`phi.cor[3,4]` = col_double()
)dat_cor <- full_join(o1[,2:8], o2[,2:8])%>%
full_join(o3[,2:8]) %>%
full_join(o4[,2:8]) %>%
full_join(prior_data1)Joining, by = 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]", "model")Joining, by = 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]", "model")
Joining, by = 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]", "model")
Joining, by = 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]", "model")plot.dat.cor <- dat_cor %>%
pivot_longer(
cols=contains('phi.cor'),
names_to = "cor",
values_to = "est"
)
sum.dat <- plot.dat.cor %>%
group_by(model, cor) %>%
summarise(
Mean = mean(est),
SD = sd(est),
Q025 = quantile(est, 0.025),
Q1 = quantile(est, 0.25),
Median = median(est),
Q3 = quantile(est, 0.75),
Q975 = quantile(est, 0.975),
)`summarise()` has grouped output by 'model'. You can override using the `.groups` argument.kable(sum.dat,format = "html", digits=3) %>%
kable_styling(full_width = T)| model | cor | Mean | SD | Q025 | Q1 | Median | Q3 | Q975 |
|---|---|---|---|---|---|---|---|---|
| model_1 | phi.cor[1,2] | 0.696 | 0.210 | 0.125 | 0.620 | 0.752 | 0.842 | 0.925 |
| model_1 | phi.cor[1,3] | 0.784 | 0.157 | 0.368 | 0.723 | 0.833 | 0.895 | 0.951 |
| model_1 | phi.cor[1,4] | 0.799 | 0.172 | 0.319 | 0.750 | 0.860 | 0.908 | 0.952 |
| model_1 | phi.cor[2,3] | 0.642 | 0.241 | 0.022 | 0.533 | 0.707 | 0.821 | 0.914 |
| model_1 | phi.cor[2,4] | 0.769 | 0.215 | 0.109 | 0.724 | 0.849 | 0.903 | 0.952 |
| model_1 | phi.cor[3,4] | 0.772 | 0.178 | 0.237 | 0.721 | 0.828 | 0.887 | 0.941 |
| model_2 | phi.cor[1,2] | 0.815 | 0.135 | 0.441 | 0.759 | 0.857 | 0.910 | 0.964 |
| model_2 | phi.cor[1,3] | 0.821 | 0.178 | 0.331 | 0.782 | 0.877 | 0.928 | 0.975 |
| model_2 | phi.cor[1,4] | 0.864 | 0.126 | 0.470 | 0.842 | 0.908 | 0.938 | 0.970 |
| model_2 | phi.cor[2,3] | 0.769 | 0.195 | 0.216 | 0.715 | 0.834 | 0.896 | 0.967 |
| model_2 | phi.cor[2,4] | 0.873 | 0.156 | 0.462 | 0.862 | 0.922 | 0.950 | 0.976 |
| model_2 | phi.cor[3,4] | 0.795 | 0.218 | 0.100 | 0.751 | 0.880 | 0.931 | 0.968 |
| model_3 | phi.cor[1,2] | 0.732 | 0.223 | 0.133 | 0.656 | 0.804 | 0.883 | 0.951 |
| model_3 | phi.cor[1,3] | 0.721 | 0.241 | 0.003 | 0.646 | 0.793 | 0.884 | 0.955 |
| model_3 | phi.cor[1,4] | 0.779 | 0.214 | 0.121 | 0.735 | 0.855 | 0.912 | 0.960 |
| model_3 | phi.cor[2,3] | 0.651 | 0.299 | -0.245 | 0.566 | 0.757 | 0.848 | 0.930 |
| model_3 | phi.cor[2,4] | 0.843 | 0.153 | 0.397 | 0.816 | 0.893 | 0.933 | 0.968 |
| model_3 | phi.cor[3,4] | 0.698 | 0.321 | -0.348 | 0.655 | 0.818 | 0.892 | 0.947 |
| model_4 | phi.cor[1,2] | 0.719 | 0.281 | -0.151 | 0.659 | 0.816 | 0.891 | 0.952 |
| model_4 | phi.cor[1,3] | 0.789 | 0.220 | 0.151 | 0.752 | 0.866 | 0.920 | 0.968 |
| model_4 | phi.cor[1,4] | 0.792 | 0.256 | -0.101 | 0.773 | 0.889 | 0.934 | 0.972 |
| model_4 | phi.cor[2,3] | 0.652 | 0.337 | -0.457 | 0.583 | 0.777 | 0.864 | 0.939 |
| model_4 | phi.cor[2,4] | 0.814 | 0.263 | -0.185 | 0.827 | 0.904 | 0.942 | 0.971 |
| model_4 | phi.cor[3,4] | 0.825 | 0.181 | 0.258 | 0.791 | 0.883 | 0.929 | 0.966 |
| prior | phi.cor[1,2] | -0.002 | 0.708 | -0.997 | -0.711 | -0.006 | 0.717 | 0.997 |
| prior | phi.cor[1,3] | -0.003 | 0.707 | -0.997 | -0.706 | -0.009 | 0.708 | 0.997 |
| prior | phi.cor[1,4] | -0.003 | 0.712 | -0.997 | -0.723 | -0.006 | 0.711 | 0.997 |
| prior | phi.cor[2,3] | 0.002 | 0.712 | -0.997 | -0.713 | 0.001 | 0.721 | 0.997 |
| prior | phi.cor[2,4] | -0.012 | 0.711 | -0.997 | -0.729 | -0.021 | 0.709 | 0.997 |
| prior | phi.cor[3,4] | 0.004 | 0.706 | -0.996 | -0.704 | 0.025 | 0.705 | 0.998 |
# cor from DWLS
cor_est <- data.frame(
cor = 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]'),
est = c(.69, .75, .78, .65, .80, .75)
)
plot.dat.cor %>%
filter(model != 'prior')%>%
ggplot(aes(x=est, y=model, group=interaction(model, cor)))+
ggdist::stat_halfeye(
adjust=2, justification=-0.1,.width=0, point_colour=NA,
normalize="all", fill="grey75"
) +
geom_boxplot(
width=.15, outlier.color = NA, alpha=0.5
) +
geom_vline(data=cor_est, aes(xintercept=est), linetype='dashed')+
labs(x="Correlation Estimates",
y="Estimating Model")+
lims(x=c(-1, 1))+
facet_wrap(.~cor)+
theme_classic()
ggplot(plot.dat.cor,aes(x=est, group=interaction(model, cor), color=model, fill=model))+
geom_density(alpha=0.25)+
facet_wrap(.~cor)+
theme_classic()
Manuscript Tables and Figures
Tables
sum.dat <- plot.dat %>%
group_by(model, factor) %>%
summarise(
Mean = mean(est),
SD = sd(est),
Q025 = quantile(est, 0.025),
Q1 = quantile(est, 0.25),
Median = median(est),
Q3 = quantile(est, 0.75),
Q975 = quantile(est, 0.975),
)`summarise()` has grouped output by 'model'. You can override using the `.groups` argument.print(
xtable(
sum.dat,
caption = c("POOLS posterior reliabilities estimates")
,align = "lrrrrrrrrr"
),
include.rownames=T,
booktabs=T
)% latex table generated in R 4.0.5 by xtable 1.8-4 package
% Wed Feb 02 13:41:30 2022
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrrrrrr}
\toprule
& model & factor & Mean & SD & Q025 & Q1 & Median & Q3 & Q975 \\
\midrule
1 & model\_1 & f1 & 0.87 & 0.01 & 0.84 & 0.86 & 0.87 & 0.87 & 0.89 \\
2 & model\_1 & f2 & 0.83 & 0.01 & 0.81 & 0.82 & 0.83 & 0.84 & 0.85 \\
3 & model\_1 & f3 & 0.65 & 0.03 & 0.60 & 0.64 & 0.66 & 0.67 & 0.70 \\
4 & model\_1 & f4 & 0.86 & 0.01 & 0.84 & 0.86 & 0.86 & 0.87 & 0.88 \\
5 & model\_2 & f1 & 0.92 & 0.02 & 0.88 & 0.91 & 0.92 & 0.93 & 0.94 \\
6 & model\_2 & f2 & 0.80 & 0.02 & 0.76 & 0.78 & 0.80 & 0.81 & 0.83 \\
7 & model\_2 & f3 & 0.87 & 0.02 & 0.83 & 0.86 & 0.87 & 0.89 & 0.91 \\
8 & model\_2 & f4 & 0.84 & 0.02 & 0.80 & 0.83 & 0.84 & 0.85 & 0.87 \\
9 & model\_3 & f1 & 0.91 & 0.01 & 0.89 & 0.90 & 0.91 & 0.92 & 0.93 \\
10 & model\_3 & f2 & 0.82 & 0.01 & 0.80 & 0.82 & 0.82 & 0.83 & 0.85 \\
11 & model\_3 & f3 & 0.85 & 0.02 & 0.81 & 0.84 & 0.85 & 0.87 & 0.89 \\
12 & model\_3 & f4 & 0.86 & 0.01 & 0.84 & 0.85 & 0.86 & 0.87 & 0.88 \\
13 & model\_4 & f1 & 0.85 & 0.02 & 0.82 & 0.84 & 0.85 & 0.86 & 0.88 \\
14 & model\_4 & f2 & 0.86 & 0.01 & 0.83 & 0.85 & 0.86 & 0.87 & 0.88 \\
15 & model\_4 & f3 & 0.68 & 0.02 & 0.63 & 0.66 & 0.68 & 0.70 & 0.73 \\
16 & model\_4 & f4 & 0.90 & 0.01 & 0.88 & 0.90 & 0.90 & 0.91 & 0.92 \\
17 & prior\_s & f1 & 0.81 & 0.08 & 0.60 & 0.76 & 0.82 & 0.87 & 0.92 \\
18 & prior\_s & f2 & 0.78 & 0.10 & 0.54 & 0.73 & 0.80 & 0.85 & 0.91 \\
19 & prior\_s & f3 & 0.78 & 0.10 & 0.54 & 0.73 & 0.80 & 0.85 & 0.91 \\
20 & prior\_s & f4 & 0.78 & 0.10 & 0.54 & 0.73 & 0.80 & 0.85 & 0.91 \\
\bottomrule
\end{tabular}
\caption{POOLS posterior reliabilities estimates}
\end{table}sum.dat <- plot.dat.cor %>%
group_by(model, cor) %>%
summarise(
Mean = mean(est),
SD = sd(est),
Q025 = quantile(est, 0.025),
Q1 = quantile(est, 0.25),
Median = median(est),
Q3 = quantile(est, 0.75),
Q975 = quantile(est, 0.975),
)`summarise()` has grouped output by 'model'. You can override using the `.groups` argument.print(
xtable(
sum.dat,
caption = c("POOLS posterior correlation estimates")
,align = "lrrrrrrrrr"
),
include.rownames=T,
booktabs=T
)% latex table generated in R 4.0.5 by xtable 1.8-4 package
% Wed Feb 02 13:41:30 2022
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrrrrrr}
\toprule
& model & cor & Mean & SD & Q025 & Q1 & Median & Q3 & Q975 \\
\midrule
1 & model\_1 & phi.cor[1,2] & 0.70 & 0.21 & 0.12 & 0.62 & 0.75 & 0.84 & 0.93 \\
2 & model\_1 & phi.cor[1,3] & 0.78 & 0.16 & 0.37 & 0.72 & 0.83 & 0.89 & 0.95 \\
3 & model\_1 & phi.cor[1,4] & 0.80 & 0.17 & 0.32 & 0.75 & 0.86 & 0.91 & 0.95 \\
4 & model\_1 & phi.cor[2,3] & 0.64 & 0.24 & 0.02 & 0.53 & 0.71 & 0.82 & 0.91 \\
5 & model\_1 & phi.cor[2,4] & 0.77 & 0.22 & 0.11 & 0.72 & 0.85 & 0.90 & 0.95 \\
6 & model\_1 & phi.cor[3,4] & 0.77 & 0.18 & 0.24 & 0.72 & 0.83 & 0.89 & 0.94 \\
7 & model\_2 & phi.cor[1,2] & 0.82 & 0.13 & 0.44 & 0.76 & 0.86 & 0.91 & 0.96 \\
8 & model\_2 & phi.cor[1,3] & 0.82 & 0.18 & 0.33 & 0.78 & 0.88 & 0.93 & 0.97 \\
9 & model\_2 & phi.cor[1,4] & 0.86 & 0.13 & 0.47 & 0.84 & 0.91 & 0.94 & 0.97 \\
10 & model\_2 & phi.cor[2,3] & 0.77 & 0.20 & 0.22 & 0.71 & 0.83 & 0.90 & 0.97 \\
11 & model\_2 & phi.cor[2,4] & 0.87 & 0.16 & 0.46 & 0.86 & 0.92 & 0.95 & 0.98 \\
12 & model\_2 & phi.cor[3,4] & 0.80 & 0.22 & 0.10 & 0.75 & 0.88 & 0.93 & 0.97 \\
13 & model\_3 & phi.cor[1,2] & 0.73 & 0.22 & 0.13 & 0.66 & 0.80 & 0.88 & 0.95 \\
14 & model\_3 & phi.cor[1,3] & 0.72 & 0.24 & 0.00 & 0.65 & 0.79 & 0.88 & 0.95 \\
15 & model\_3 & phi.cor[1,4] & 0.78 & 0.21 & 0.12 & 0.73 & 0.86 & 0.91 & 0.96 \\
16 & model\_3 & phi.cor[2,3] & 0.65 & 0.30 & -0.25 & 0.57 & 0.76 & 0.85 & 0.93 \\
17 & model\_3 & phi.cor[2,4] & 0.84 & 0.15 & 0.40 & 0.82 & 0.89 & 0.93 & 0.97 \\
18 & model\_3 & phi.cor[3,4] & 0.70 & 0.32 & -0.35 & 0.66 & 0.82 & 0.89 & 0.95 \\
19 & model\_4 & phi.cor[1,2] & 0.72 & 0.28 & -0.15 & 0.66 & 0.82 & 0.89 & 0.95 \\
20 & model\_4 & phi.cor[1,3] & 0.79 & 0.22 & 0.15 & 0.75 & 0.87 & 0.92 & 0.97 \\
21 & model\_4 & phi.cor[1,4] & 0.79 & 0.26 & -0.10 & 0.77 & 0.89 & 0.93 & 0.97 \\
22 & model\_4 & phi.cor[2,3] & 0.65 & 0.34 & -0.46 & 0.58 & 0.78 & 0.86 & 0.94 \\
23 & model\_4 & phi.cor[2,4] & 0.81 & 0.26 & -0.18 & 0.83 & 0.90 & 0.94 & 0.97 \\
24 & model\_4 & phi.cor[3,4] & 0.82 & 0.18 & 0.26 & 0.79 & 0.88 & 0.93 & 0.97 \\
25 & prior & phi.cor[1,2] & -0.00 & 0.71 & -1.00 & -0.71 & -0.01 & 0.72 & 1.00 \\
26 & prior & phi.cor[1,3] & -0.00 & 0.71 & -1.00 & -0.71 & -0.01 & 0.71 & 1.00 \\
27 & prior & phi.cor[1,4] & -0.00 & 0.71 & -1.00 & -0.72 & -0.01 & 0.71 & 1.00 \\
28 & prior & phi.cor[2,3] & 0.00 & 0.71 & -1.00 & -0.71 & 0.00 & 0.72 & 1.00 \\
29 & prior & phi.cor[2,4] & -0.01 & 0.71 & -1.00 & -0.73 & -0.02 & 0.71 & 1.00 \\
30 & prior & phi.cor[3,4] & 0.00 & 0.71 & -1.00 & -0.70 & 0.02 & 0.70 & 1.00 \\
\bottomrule
\end{tabular}
\caption{POOLS posterior correlation estimates}
\end{table}Figures
reli_est <- data.frame(
factor = c('EfL', 'SC', 'IN', 'EnL'),
est = c(.82, .79, .80, .78)
)
p <- plot.dat %>%
filter(model != 'prior_s') %>%
mutate(model = factor(model, levels=rev(paste0('model_',1:4)),
labels=rev(paste0('Model ', 1:4))),
factor = factor(factor, levels=c('f1','f2','f3','f4'),
labels=c('EfL','SC','IN','EnL')))%>%
ggplot(aes(x=est, y=model, group=model))+
ggdist::stat_halfeye(
adjust=2, justification=-0.1,.width=0, point_colour=NA,
normalize="all", fill="grey75"
) +
geom_boxplot(
width=.15, outlier.color = NA, alpha=0.5
) +
geom_vline(data=reli_est, aes(xintercept=est), linetype='dashed')+
#annotate('text', x=0.6,y=4.5,label=expression(omega^2==.8))+
labs(x= expression(paste("Reliability Posterior (", omega^2, ")")),
y=NULL)+
lims(x=c(0.5, 1))+
facet_wrap(.~factor, nrow=1)+
theme_bw() +
theme(panel.grid = element_blank(),
strip.background = element_blank())
p
ggsave(filename = "fig/pools_posterior_omega.pdf",plot=p,width = 7, height=3,units="in")
ggsave(filename = "fig/pools_posterior_omega.png",plot=p,width = 7, height=3,units="in")
ggsave(filename = "fig/pools_posterior_omega.eps",plot=p,width = 7, height=3,units="in")Warning in grid.Call.graphics(C_polygon, x$x, x$y, index): semi-transparency is
not supported on this device: reported only once per page## correlation results
cor_est <- data.frame(
cor = c(
'cor(EfL,SC)','cor(EfL,IN)','cor(EfL,EnL)',
'cor(SC,IN)','cor(SC,EnL)','cor(IN,EnL)'),
est = c(.69, .75, .78, .65, .80, .75)
)
p <- plot.dat.cor %>%
filter(model != 'prior')%>%
mutate(
model = factor(model, levels=rev(paste0('model_',1:4)),
labels=rev(paste0('Model ', 1:4))),
cor = factor(cor, levels=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]'),
labels=c(
'cor(EfL,SC)','cor(EfL,IN)','cor(EfL,EnL)',
'cor(SC,IN)','cor(SC,EnL)','cor(IN,EnL)')
)
) %>%
ggplot(aes(x=est, y=model, group=interaction(model, cor)))+
ggdist::stat_halfeye(
adjust=2, justification=-0.1,.width=0, point_colour=NA,
normalize="all", fill="grey75"
) +
geom_boxplot(
width=.15, outlier.color = NA, alpha=0.5
) +
geom_vline(data=cor_est, aes(xintercept=est), linetype='dashed')+
labs(x="Correlation Estimates",
y=NULL)+
lims(x=c(0, 1))+
facet_wrap(.~cor)+
theme_classic()
pWarning: Removed 214 rows containing missing values (stat_slabinterval).Warning: Removed 195 rows containing missing values (stat_slabinterval).Warning: Removed 276 rows containing missing values (stat_slabinterval).Warning: Removed 374 rows containing missing values (stat_slabinterval).Warning: Removed 270 rows containing missing values (stat_slabinterval).Warning: Removed 617 rows containing missing values (stat_slabinterval).Warning: Removed 1946 rows containing non-finite values (stat_boxplot).
ggsave(filename = "fig/pools_posterior_phi.pdf",plot=p,width = 7, height=4.5,units="in")Warning: Removed 214 rows containing missing values (stat_slabinterval).Warning: Removed 195 rows containing missing values (stat_slabinterval).Warning: Removed 276 rows containing missing values (stat_slabinterval).Warning: Removed 374 rows containing missing values (stat_slabinterval).Warning: Removed 270 rows containing missing values (stat_slabinterval).Warning: Removed 617 rows containing missing values (stat_slabinterval).Warning: Removed 1946 rows containing non-finite values (stat_boxplot).ggsave(filename = "fig/pools_posterior_phi.png",plot=p,width = 7, height=4.5,units="in")Warning: Removed 214 rows containing missing values (stat_slabinterval).Warning: Removed 195 rows containing missing values (stat_slabinterval).Warning: Removed 276 rows containing missing values (stat_slabinterval).Warning: Removed 374 rows containing missing values (stat_slabinterval).Warning: Removed 270 rows containing missing values (stat_slabinterval).Warning: Removed 617 rows containing missing values (stat_slabinterval).Warning: Removed 1946 rows containing non-finite values (stat_boxplot).ggsave(filename = "fig/pools_posterior_phi.eps",plot=p,width = 7, height=4.5,units="in")Warning: Removed 214 rows containing missing values (stat_slabinterval).Warning: Removed 195 rows containing missing values (stat_slabinterval).Warning: Removed 276 rows containing missing values (stat_slabinterval).Warning: Removed 374 rows containing missing values (stat_slabinterval).Warning: Removed 270 rows containing missing values (stat_slabinterval).Warning: Removed 617 rows containing missing values (stat_slabinterval).Warning: Removed 1946 rows containing non-finite values (stat_boxplot).Warning in grid.Call.graphics(C_polygon, x$x, x$y, index): semi-transparency is
not supported on this device: reported only once per page
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
[4] rio_0.5.26 ellipsis_0.3.1 ggridges_0.5.3
[7] rprojroot_2.0.2 fs_1.5.0 rstudioapi_0.13
[10] 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
[16] knitr_1.31 jsonlite_1.7.2 nloptr_1.2.2.2
[19] broom_0.7.5 dbplyr_2.1.0 ggdist_3.0.1
[22] compiler_4.0.5 httr_1.4.2 backports_1.2.1
[25] assertthat_0.2.1 cli_2.3.1 later_1.1.0.1
[28] htmltools_0.5.1.1 tools_4.0.5 gtable_0.3.0
[31] glue_1.4.2 Rcpp_1.0.7 cellranger_1.1.0
[34] jquerylib_0.1.3 vctrs_0.3.6 svglite_2.0.0
[37] nlme_3.1-152 psych_2.0.12 xfun_0.21
[40] ps_1.6.0 openxlsx_4.2.3 rvest_1.0.0
[43] lifecycle_1.0.0 MASS_7.3-53.1 scales_1.1.1
[46] 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
[52] yaml_2.2.1 sass_0.3.1 reshape_0.8.8
[55] stringi_1.5.3 highr_0.8 zip_2.1.1
[58] boot_1.3-27 rlang_0.4.10 pkgconfig_2.0.3
[61] systemfonts_1.0.1 distributional_0.3.0 evaluate_0.14
[64] lattice_0.20-41 labeling_0.4.2 tidyselect_1.1.0
[67] GGally_2.1.1 plyr_1.8.6 magrittr_2.0.1
[70] R6_2.5.0 generics_0.1.0 DBI_1.1.1
[73] foreign_0.8-81 pillar_1.5.1 haven_2.3.1
[76] withr_2.4.1 abind_1.4-5 modelr_0.1.8
[79] crayon_1.4.1 utf8_1.1.4 tmvnsim_1.0-2
[82] rmarkdown_2.7 grid_4.0.5 CDM_7.5-15
[85] pbivnorm_0.6.0 git2r_0.28.0 reprex_1.0.0
[88] digest_0.6.27 webshot_0.5.2 httpuv_1.5.5
[91] textshaping_0.3.1 stats4_4.0.5 munsell_0.5.0
[94] viridisLite_0.3.0 bslib_0.2.4 R2WinBUGS_2.1-21