Monte Carlo Simulation
R. Noah Padgett
2020-10-22
Last updated: 2020-11-14
Checks: 6 1
Knit directory: laplace-local-fit/
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| Rmd | f54bc3f | noah-padgett | 2020-10-23 | updated simulation |
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| Rmd | 40d9a1e | noah-padgett | 2020-10-15 | updated publication figure |
Proposed Model with Analytic Expectations
lambda <- matrix(
c(rep(0.8,4), rep(0,4), rep(0,4),
rep(0,4), rep(0.8,4), rep(0,4),
rep(0,4), rep(0,4), rep(0.8,4)),
ncol=3
)
phi <- matrix(
c(1, 0.3, 0.1,
0.3, 1, 0.2,
0.1, 0.2, 1),
ncol=3
)
psi <- diag(1, ncol=12, nrow=12)
psi[2, 3] <- psi[3, 2] <- 0.25
psi[4, 7] <- psi[7, 4] <- 0.3
sigma <- lambda%*%phi%*%t(lambda) + psi
sigma [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 1.640 0.640 0.640 0.640 0.192 0.192 0.192 0.192 0.064 0.064 0.064 0.064
[2,] 0.640 1.640 0.890 0.640 0.192 0.192 0.192 0.192 0.064 0.064 0.064 0.064
[3,] 0.640 0.890 1.640 0.640 0.192 0.192 0.192 0.192 0.064 0.064 0.064 0.064
[4,] 0.640 0.640 0.640 1.640 0.192 0.192 0.492 0.192 0.064 0.064 0.064 0.064
[5,] 0.192 0.192 0.192 0.192 1.640 0.640 0.640 0.640 0.128 0.128 0.128 0.128
[6,] 0.192 0.192 0.192 0.192 0.640 1.640 0.640 0.640 0.128 0.128 0.128 0.128
[7,] 0.192 0.192 0.192 0.492 0.640 0.640 1.640 0.640 0.128 0.128 0.128 0.128
[8,] 0.192 0.192 0.192 0.192 0.640 0.640 0.640 1.640 0.128 0.128 0.128 0.128
[9,] 0.064 0.064 0.064 0.064 0.128 0.128 0.128 0.128 1.640 0.640 0.640 0.640
[10,] 0.064 0.064 0.064 0.064 0.128 0.128 0.128 0.128 0.640 1.640 0.640 0.640
[11,] 0.064 0.064 0.064 0.064 0.128 0.128 0.128 0.128 0.640 0.640 1.640 0.640
[12,] 0.064 0.064 0.064 0.064 0.128 0.128 0.128 0.128 0.640 0.640 0.640 1.640psi_m <- diag(1, ncol=12, nrow=12)
sigma_m <- lambda%*%phi%*%t(lambda) + psi_m
sigma_m - sigma [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0 0.00 0.00 0.0 0 0 0.0 0 0 0 0 0
[2,] 0 0.00 -0.25 0.0 0 0 0.0 0 0 0 0 0
[3,] 0 -0.25 0.00 0.0 0 0 0.0 0 0 0 0 0
[4,] 0 0.00 0.00 0.0 0 0 -0.3 0 0 0 0 0
[5,] 0 0.00 0.00 0.0 0 0 0.0 0 0 0 0 0
[6,] 0 0.00 0.00 0.0 0 0 0.0 0 0 0 0 0
[7,] 0 0.00 0.00 -0.3 0 0 0.0 0 0 0 0 0
[8,] 0 0.00 0.00 0.0 0 0 0.0 0 0 0 0 0
[9,] 0 0.00 0.00 0.0 0 0 0.0 0 0 0 0 0
[10,] 0 0.00 0.00 0.0 0 0 0.0 0 0 0 0 0
[11,] 0 0.00 0.00 0.0 0 0 0.0 0 0 0 0 0
[12,] 0 0.00 0.00 0.0 0 0 0.0 0 0 0 0 0Simulation Study
# specify population model
population.model <- '
f1 =~ 0.8*y1 + 0.8*y2 + 0.8*y3 + 0.8*y4
f2 =~ 0.8*y5 + 0.8*y6 + 0.8*y7 + 0.8*y8
f3 =~ 0.8*y9 + 0.8*y10 + 0.8*y11 + 0.8*y12
# Factor (co)variances
f1 ~~ 1*f1 + 0.3*f2 + 0.1*f3
f2 ~~ 1*f2 + 0.2*f3
f3 ~~ 1*f3
# residual covariances
y2 ~~ 0.1*y3
y4 ~~ 0.3*y7
'
# analysis model
analysis.model1 <- '
f1 =~ NA*y1 + y2 + y3 + y4
f2 =~ NA*y5 + y6 + y7 + y8
f3 =~ NA*y9 + y10 + y11 + y12
# Factor covariances
f1 ~~ 1*f1 + f2 + f3
f2 ~~ 1*f2 + f3
f3 ~~ 1*f3
# residual covariances
y2 ~~ y3
y4 ~~ y7
'
Output <- sim(nrep, model=analysis.model1,silent = T,
n=500, generate=population.model,
lavaanfun = "cfa")
sim.res[,1:2] <- Output@stdCoef[, c("y2~~y3", "y4~~y7")]
colnames(sim.res) <- c("y2~~y3", "y4~~y7")
plot_dat <- sim.res %>%
pivot_longer(
cols=everything(),
names_to = "Parameter",
values_to = "Estimate"
) %>%
mutate(V = ifelse(Parameter %like% "=~", 0.30, 0.25))
p <- ggplot(plot_dat, aes(x=Estimate))+
geom_density(adjust = 2)+
geom_vline(aes(xintercept = V), linetype="dashed")+
#geom_vline(aes(xintercept = -V), linetype="dashed")+
facet_wrap(.~Parameter)+
theme_bw()
p
mean(sim.res[,1] > 0.25)[1] 0.01mean(sim.res[,2] > 0.25)[1] 0.83Laplace Approximation
wd <- getwd()
source(paste0(wd, "/code/utility_functions.R"))
|
| | 0%source(paste0(wd, "/code/laplace_functions.R"))
# specify population model
population.model <- '
f1 =~ 0.8*y1 + 0.8*y2 + 0.8*y3 + 0.8*y4
f2 =~ 0.8*y5 + 0.8*y6 + 0.8*y7 + 0.8*y8
f3 =~ 0.8*y9 + 0.8*y10 + 0.8*y11 + 0.8*y12
# Factor (co)variances
f1 ~~ 1*f1 + 0.3*f2 + 0.1*f3
f2 ~~ 1*f2 + 0.2*f3
f3 ~~ 1*f3
# residual covariances
y4 ~~ 0.3*y7
y2 ~~ 0.1*y3
'
# generate data
myData <- simulateData(population.model, sample.nobs=500L)
# fit model
myModel <- '
f1 =~ y1 + y2 + y3 + y4
f2 =~ y5 + y6 + y7 + y8
f3 =~ y9 + y10 + y11 + y12
# Factor covariances
f1 ~~ f2 + f3
f2 ~~ f3
'
fit <- cfa(myModel, data=myData)
summary(fit, standardized=T)lavaan 0.6-7 ended normally after 37 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 27
Number of observations 500
Model Test User Model:
Test statistic 124.338
Degrees of freedom 51
P-value (Chi-square) 0.000
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
f1 =~
y1 1.000 0.829 0.626
y2 0.915 0.096 9.512 0.000 0.758 0.620
y3 0.928 0.099 9.384 0.000 0.769 0.604
y4 0.961 0.101 9.469 0.000 0.796 0.615
f2 =~
y5 1.000 0.723 0.600
y6 1.134 0.119 9.563 0.000 0.820 0.655
y7 0.988 0.113 8.715 0.000 0.715 0.550
y8 1.091 0.115 9.460 0.000 0.789 0.638
f3 =~
y9 1.000 0.773 0.605
y10 1.000 0.112 8.896 0.000 0.773 0.604
y11 0.982 0.108 9.053 0.000 0.758 0.631
y12 0.932 0.106 8.751 0.000 0.720 0.584
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
f1 ~~
f2 0.253 0.046 5.507 0.000 0.422 0.422
f3 0.047 0.041 1.155 0.248 0.074 0.074
f2 ~~
f3 0.132 0.038 3.451 0.001 0.236 0.236
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.y1 1.064 0.091 11.728 0.000 1.064 0.608
.y2 0.920 0.078 11.860 0.000 0.920 0.615
.y3 1.030 0.085 12.184 0.000 1.030 0.635
.y4 1.044 0.087 11.974 0.000 1.044 0.622
.y5 0.930 0.076 12.290 0.000 0.930 0.640
.y6 0.894 0.081 11.076 0.000 0.894 0.571
.y7 1.178 0.090 13.118 0.000 1.178 0.697
.y8 0.908 0.079 11.502 0.000 0.908 0.594
.y9 1.034 0.088 11.793 0.000 1.034 0.634
.y10 1.039 0.088 11.814 0.000 1.039 0.635
.y11 0.869 0.078 11.195 0.000 0.869 0.602
.y12 1.001 0.082 12.222 0.000 1.001 0.659
f1 0.687 0.107 6.421 0.000 1.000 1.000
f2 0.523 0.085 6.142 0.000 1.000 1.000
f3 0.597 0.099 6.039 0.000 1.000 1.000lfit <- laplace_local_fit(
fit, data=myData, cut.load = 0.30, cut.cov = 0.25,
standardize = T, pb = F,
opt=list(scale.cov=1, no.samples=10000))library(kableExtra)
kable(lfit$Summary, format="html", digits=3) %>%
kable_styling(full_width = T) %>%
scroll_box(width="100%", height = "500px")| Parameter | Pr(|theta|>cutoff) | mean | sd | p0.025 | p0.25 | p0.5 | p0.75 | p0.975 |
|---|---|---|---|---|---|---|---|---|
| y7~~y4 | 0.998 | 0.372 | 0.043 | 0.289 | 0.342 | 0.372 | 0.401 | 0.458 |
| f1=~y7 | 0.014 | 0.158 | 0.064 | 0.033 | 0.114 | 0.158 | 0.201 | 0.285 |
| y5~~y1 | 0.012 | 0.133 | 0.052 | 0.031 | 0.099 | 0.133 | 0.168 | 0.235 |
| y11~~y4 | 0.009 | -0.125 | 0.054 | -0.229 | -0.162 | -0.125 | -0.088 | -0.019 |
| f1=~y6 | 0.005 | -0.136 | 0.063 | -0.259 | -0.177 | -0.136 | -0.094 | -0.010 |
| y9~~y7 | 0.004 | 0.118 | 0.051 | 0.019 | 0.084 | 0.118 | 0.152 | 0.217 |
| y7~~y6 | 0.003 | -0.098 | 0.056 | -0.207 | -0.136 | -0.099 | -0.060 | 0.012 |
| f3=~y4 | 0.003 | -0.117 | 0.067 | -0.246 | -0.162 | -0.117 | -0.072 | 0.015 |
| f2=~y9 | 0.003 | 0.117 | 0.067 | -0.015 | 0.071 | 0.117 | 0.163 | 0.249 |
| y12~~y6 | 0.003 | 0.102 | 0.054 | -0.002 | 0.066 | 0.102 | 0.138 | 0.208 |
| y5~~y4 | 0.003 | -0.098 | 0.053 | -0.203 | -0.134 | -0.098 | -0.062 | 0.004 |
| y4~~y3 | 0.003 | -0.097 | 0.056 | -0.206 | -0.135 | -0.097 | -0.059 | 0.016 |
| y12~~y3 | 0.003 | 0.105 | 0.052 | 0.004 | 0.071 | 0.105 | 0.141 | 0.209 |
| y12~~y8 | 0.003 | -0.104 | 0.053 | -0.208 | -0.140 | -0.104 | -0.068 | 0.001 |
| f2=~y4 | 0.002 | 0.105 | 0.068 | -0.028 | 0.060 | 0.105 | 0.151 | 0.240 |
| y6~~y4 | 0.002 | -0.093 | 0.055 | -0.201 | -0.130 | -0.092 | -0.057 | 0.015 |
| y2~~y1 | 0.002 | -0.091 | 0.056 | -0.200 | -0.129 | -0.091 | -0.053 | 0.021 |
| f2=~y3 | 0.001 | -0.102 | 0.067 | -0.235 | -0.148 | -0.103 | -0.056 | 0.030 |
| f3=~y3 | 0.001 | 0.097 | 0.067 | -0.035 | 0.052 | 0.097 | 0.142 | 0.228 |
| y3~~y2 | 0.001 | 0.105 | 0.048 | 0.011 | 0.072 | 0.104 | 0.137 | 0.199 |
| y11~~y7 | 0.001 | -0.082 | 0.053 | -0.185 | -0.117 | -0.082 | -0.045 | 0.021 |
| y7~~y2 | 0.001 | -0.087 | 0.053 | -0.191 | -0.122 | -0.087 | -0.052 | 0.017 |
| y8~~y5 | 0.001 | -0.079 | 0.055 | -0.186 | -0.117 | -0.079 | -0.042 | 0.031 |
| f1=~y11 | 0.001 | -0.108 | 0.062 | -0.231 | -0.150 | -0.108 | -0.066 | 0.015 |
| f2=~y1 | 0.001 | 0.081 | 0.068 | -0.052 | 0.035 | 0.080 | 0.128 | 0.215 |
| f2=~y2 | 0.000 | -0.090 | 0.066 | -0.219 | -0.134 | -0.090 | -0.045 | 0.039 |
| y7~~y3 | 0.000 | -0.079 | 0.052 | -0.182 | -0.115 | -0.079 | -0.044 | 0.020 |
| f1=~y9 | 0.000 | 0.079 | 0.064 | -0.049 | 0.036 | 0.079 | 0.122 | 0.204 |
| f3=~y6 | 0.000 | 0.085 | 0.066 | -0.046 | 0.041 | 0.085 | 0.130 | 0.214 |
| y9~~y6 | 0.000 | -0.046 | 0.055 | -0.154 | -0.083 | -0.045 | -0.008 | 0.064 |
| y10~~y3 | 0.000 | -0.064 | 0.053 | -0.169 | -0.100 | -0.064 | -0.028 | 0.040 |
| f3=~y1 | 0.000 | 0.057 | 0.068 | -0.076 | 0.012 | 0.057 | 0.103 | 0.191 |
| y10~~y1 | 0.000 | 0.060 | 0.054 | -0.044 | 0.024 | 0.060 | 0.097 | 0.165 |
| y5~~y3 | 0.000 | -0.064 | 0.053 | -0.170 | -0.100 | -0.065 | -0.028 | 0.040 |
| y6~~y5 | 0.000 | 0.050 | 0.051 | -0.050 | 0.016 | 0.051 | 0.084 | 0.149 |
| y8~~y6 | 0.000 | 0.079 | 0.050 | -0.019 | 0.045 | 0.079 | 0.112 | 0.179 |
| y12~~y9 | 0.000 | -0.049 | 0.054 | -0.155 | -0.085 | -0.048 | -0.012 | 0.058 |
| f3=~y2 | 0.000 | -0.043 | 0.065 | -0.169 | -0.087 | -0.043 | 0.002 | 0.085 |
| f3=~y8 | 0.000 | -0.066 | 0.066 | -0.196 | -0.111 | -0.066 | -0.022 | 0.061 |
| y9~~y1 | 0.000 | 0.031 | 0.054 | -0.076 | -0.006 | 0.031 | 0.068 | 0.137 |
| y9~~y2 | 0.000 | -0.053 | 0.054 | -0.159 | -0.089 | -0.054 | -0.017 | 0.053 |
| y10~~y7 | 0.000 | -0.061 | 0.052 | -0.163 | -0.096 | -0.061 | -0.026 | 0.042 |
| f1=~y5 | 0.000 | 0.011 | 0.061 | -0.110 | -0.030 | 0.011 | 0.052 | 0.130 |
| f1=~y8 | 0.000 | 0.004 | 0.063 | -0.120 | -0.039 | 0.004 | 0.047 | 0.127 |
| f1=~y10 | 0.000 | -0.015 | 0.064 | -0.142 | -0.058 | -0.014 | 0.029 | 0.110 |
| f1=~y12 | 0.000 | 0.053 | 0.062 | -0.067 | 0.012 | 0.054 | 0.095 | 0.174 |
| f2=~y10 | 0.000 | -0.049 | 0.068 | -0.184 | -0.095 | -0.049 | -0.003 | 0.080 |
| f2=~y11 | 0.000 | -0.062 | 0.066 | -0.190 | -0.108 | -0.062 | -0.017 | 0.067 |
| f2=~y12 | 0.000 | 0.000 | 0.066 | -0.130 | -0.044 | -0.001 | 0.044 | 0.127 |
| f3=~y5 | 0.000 | 0.000 | 0.064 | -0.126 | -0.043 | 0.000 | 0.044 | 0.125 |
| f3=~y7 | 0.000 | -0.026 | 0.067 | -0.157 | -0.070 | -0.026 | 0.019 | 0.104 |
| y3~~y1 | 0.000 | 0.026 | 0.051 | -0.072 | -0.008 | 0.027 | 0.060 | 0.128 |
| y4~~y1 | 0.000 | 0.018 | 0.052 | -0.086 | -0.017 | 0.019 | 0.053 | 0.118 |
| y6~~y1 | 0.000 | -0.020 | 0.055 | -0.126 | -0.058 | -0.021 | 0.017 | 0.089 |
| y7~~y1 | 0.000 | -0.024 | 0.053 | -0.128 | -0.059 | -0.024 | 0.012 | 0.077 |
| y8~~y1 | 0.000 | -0.002 | 0.055 | -0.109 | -0.039 | -0.002 | 0.035 | 0.104 |
| y11~~y1 | 0.000 | -0.033 | 0.055 | -0.140 | -0.070 | -0.033 | 0.004 | 0.076 |
| y12~~y1 | 0.000 | -0.018 | 0.054 | -0.121 | -0.054 | -0.019 | 0.019 | 0.088 |
| y4~~y2 | 0.000 | 0.016 | 0.052 | -0.085 | -0.019 | 0.016 | 0.051 | 0.117 |
| y5~~y2 | 0.000 | 0.032 | 0.053 | -0.073 | -0.004 | 0.032 | 0.068 | 0.135 |
| y6~~y2 | 0.000 | -0.033 | 0.055 | -0.143 | -0.071 | -0.033 | 0.004 | 0.075 |
| y8~~y2 | 0.000 | -0.018 | 0.055 | -0.125 | -0.055 | -0.018 | 0.020 | 0.090 |
| y10~~y2 | 0.000 | -0.009 | 0.054 | -0.116 | -0.045 | -0.009 | 0.027 | 0.097 |
| y11~~y2 | 0.000 | 0.019 | 0.055 | -0.090 | -0.018 | 0.019 | 0.056 | 0.127 |
| y12~~y2 | 0.000 | 0.024 | 0.053 | -0.082 | -0.011 | 0.024 | 0.060 | 0.130 |
| y6~~y3 | 0.000 | -0.028 | 0.055 | -0.137 | -0.065 | -0.028 | 0.009 | 0.078 |
| y8~~y3 | 0.000 | 0.014 | 0.054 | -0.092 | -0.023 | 0.014 | 0.051 | 0.118 |
| y9~~y3 | 0.000 | 0.050 | 0.053 | -0.055 | 0.014 | 0.050 | 0.086 | 0.155 |
| y11~~y3 | 0.000 | 0.040 | 0.054 | -0.067 | 0.003 | 0.039 | 0.077 | 0.145 |
| y8~~y4 | 0.000 | 0.010 | 0.054 | -0.094 | -0.026 | 0.011 | 0.046 | 0.116 |
| y9~~y4 | 0.000 | 0.009 | 0.054 | -0.097 | -0.027 | 0.009 | 0.046 | 0.115 |
| y10~~y4 | 0.000 | 0.013 | 0.053 | -0.092 | -0.023 | 0.014 | 0.049 | 0.118 |
| y12~~y4 | 0.000 | -0.041 | 0.053 | -0.145 | -0.076 | -0.041 | -0.005 | 0.063 |
| y7~~y5 | 0.000 | 0.025 | 0.051 | -0.075 | -0.009 | 0.026 | 0.060 | 0.124 |
| y9~~y5 | 0.000 | 0.050 | 0.053 | -0.054 | 0.014 | 0.051 | 0.085 | 0.154 |
| y10~~y5 | 0.000 | -0.055 | 0.053 | -0.160 | -0.091 | -0.055 | -0.019 | 0.047 |
| y11~~y5 | 0.000 | 0.037 | 0.054 | -0.069 | 0.000 | 0.037 | 0.075 | 0.142 |
| y12~~y5 | 0.000 | -0.035 | 0.053 | -0.139 | -0.071 | -0.036 | 0.000 | 0.069 |
| y10~~y6 | 0.000 | 0.011 | 0.055 | -0.097 | -0.026 | 0.011 | 0.049 | 0.120 |
| y11~~y6 | 0.000 | 0.019 | 0.055 | -0.087 | -0.019 | 0.019 | 0.056 | 0.128 |
| y8~~y7 | 0.000 | 0.002 | 0.052 | -0.101 | -0.034 | 0.002 | 0.036 | 0.102 |
| y12~~y7 | 0.000 | 0.003 | 0.052 | -0.100 | -0.032 | 0.002 | 0.037 | 0.105 |
| y9~~y8 | 0.000 | -0.006 | 0.054 | -0.114 | -0.043 | -0.007 | 0.031 | 0.101 |
| y10~~y8 | 0.000 | 0.038 | 0.054 | -0.065 | 0.001 | 0.037 | 0.074 | 0.142 |
| y11~~y8 | 0.000 | -0.002 | 0.055 | -0.109 | -0.039 | -0.002 | 0.034 | 0.104 |
| y10~~y9 | 0.000 | -0.004 | 0.053 | -0.106 | -0.040 | -0.004 | 0.032 | 0.099 |
| y11~~y9 | 0.000 | 0.020 | 0.053 | -0.082 | -0.015 | 0.020 | 0.055 | 0.125 |
| y11~~y10 | 0.000 | -0.021 | 0.054 | -0.126 | -0.058 | -0.021 | 0.016 | 0.088 |
| y12~~y10 | 0.000 | 0.037 | 0.051 | -0.062 | 0.002 | 0.037 | 0.072 | 0.135 |
| y12~~y11 | 0.000 | 0.011 | 0.053 | -0.093 | -0.025 | 0.011 | 0.047 | 0.115 |
Sampling Distributions
# transform
plot_dat_laplace <- lfit$`All Results` %>%
pivot_longer(
cols=everything(),
names_to = "Parameter",
values_to = "Estimate"
)
plot_dat_laplace <- filter(plot_dat_laplace,
Parameter %in% c("y3~~y2", "y7~~y4")) %>%
mutate(V = ifelse(Parameter %like% "=~", 0.30, 0.25),
Parameter = ifelse(Parameter == "y3~~y2",
"cov(y[2], y[3])", "cov(y[4], y[7])"))
plot_dat_emp <- sim.res %>%
pivot_longer(
cols=everything(),
names_to = "Parameter",
values_to = "Estimate"
) %>%
mutate(V = ifelse(Parameter %like% "=~", 0.30, 0.25),
Parameter = ifelse(Parameter == "y2~~y3",
"cov(y[2], y[3])", "cov(y[4], y[7])"))
lty <- c("True" = 1, "Laplace" = 2)
p <- ggplot()+
geom_density(data=plot_dat_emp, adjust = 2,
aes(x=Estimate, linetype="True"))+
geom_density(data=plot_dat_laplace, adjust = 2,
aes(x=Estimate, linetype="Laplace"))+
#geom_vline(xintercept = 0.25, linetype="dotted")+
scale_linetype_manual(values = lty, name=NULL)+
facet_wrap(.~Parameter, labeller = label_parsed)+
theme_bw() +
theme(panel.grid = element_blank(),
strip.background = element_blank(),
legend.position = "bottom",
text=element_text(size=13))
p
ggsave("manuscript/fig/sampling_dist.pdf", p, units="in", width=7, height=3.5)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
Random number generation:
RNG: L'Ecuyer-CMRG
Normal: Inversion
Sample: Rejection
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] tcltk stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] kableExtra_1.1.0 data.table_1.13.0 mvtnorm_1.1-1 coda_0.19-3
[5] simsem_0.5-15 lavaan_0.6-7 ggplot2_3.3.2 dplyr_1.0.1
[9] tidyr_1.1.1 xtable_1.8-4 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.19 purrr_0.3.4 lattice_0.20-41
[5] colorspace_1.4-1 vctrs_0.3.2 generics_0.0.2 viridisLite_0.3.0
[9] htmltools_0.5.0 stats4_4.0.2 yaml_2.2.1 rlang_0.4.7
[13] later_1.1.0.1 pillar_1.4.6 glue_1.4.1 withr_2.2.0
[17] lifecycle_0.2.0 stringr_1.4.0 munsell_0.5.0 gtable_0.3.0
[21] rvest_0.3.6 evaluate_0.14 labeling_0.3 knitr_1.29
[25] httpuv_1.5.4 parallel_4.0.2 highr_0.8 Rcpp_1.0.5
[29] readr_1.3.1 promises_1.1.1 backports_1.1.7 scales_1.1.1
[33] webshot_0.5.2 tmvnsim_1.0-2 farver_2.0.3 fs_1.5.0
[37] mnormt_2.0.2 hms_0.5.3 digest_0.6.25 stringi_1.4.6
[41] grid_4.0.2 rprojroot_1.3-2 tools_4.0.2 magrittr_1.5
[45] tibble_3.0.3 crayon_1.3.4 whisker_0.4 pbivnorm_0.6.0
[49] pkgconfig_2.0.3 ellipsis_0.3.1 MASS_7.3-51.6 xml2_1.3.2
[53] httr_1.4.2 rmarkdown_2.5 rstudioapi_0.11 R6_2.4.1
[57] git2r_0.27.1 compiler_4.0.2