ML-CFA: Parameter Bias - Factor Loadings
Last updated: 2020-06-10
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rm(list=ls())
source(paste0(getwd(),"/code/load_packages.R"))
#source(paste0(getwd(),"/code/get_data.R"))sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
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] xtable_1.8-4 kableExtra_1.1.0 cowplot_1.0.0
[4] MplusAutomation_0.7-3 data.table_1.12.8 patchwork_1.0.0
[7] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[10] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[13] tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0
[16] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 lubridate_1.7.8 lattice_0.20-38 assertthat_0.2.1
[5] rprojroot_1.3-2 digest_0.6.25 R6_2.4.1 cellranger_1.1.0
[9] plyr_1.8.6 backports_1.1.7 reprex_0.3.0 evaluate_0.14
[13] coda_0.19-3 httr_1.4.1 pillar_1.4.4 rlang_0.4.6
[17] readxl_1.3.1 rstudioapi_0.11 whisker_0.4 blob_1.2.1
[21] texreg_1.36.23 gsubfn_0.7 rmarkdown_2.1 proto_1.0.0
[25] webshot_0.5.2 pander_0.6.3 munsell_0.5.0 broom_0.5.6
[29] compiler_3.6.3 httpuv_1.5.2 modelr_0.1.8 xfun_0.14
[33] pkgconfig_2.0.3 htmltools_0.4.0 tidyselect_1.1.0 viridisLite_0.3.0
[37] fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4 withr_2.2.0
[41] later_1.0.0 grid_3.6.3 nlme_3.1-144 jsonlite_1.6.1
[45] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1
[49] magrittr_1.5 scales_1.1.1 cli_2.0.2 stringi_1.4.6
[53] fs_1.4.1 promises_1.1.0 xml2_1.3.2 ellipsis_0.3.1
[57] generics_0.0.2 vctrs_0.3.0 boot_1.3-24 tools_3.6.3
[61] glue_1.4.1 hms_0.5.3 parallel_3.6.3 yaml_2.2.1
[65] colorspace_1.4-1 rvest_0.3.5 knitr_1.28 haven_2.3.0 # general options
theme_set(theme_bw())
options(digits=3)# set up vectors of variable names
pvec <- c(paste0('lambda1',1:6), paste0('lambda2',6:10), 'psiW12','psiB1', 'psiB2', 'psiB12', paste0('thetaB',1:10), 'icc_lv1_est', 'icc_lv2_est', paste0('icc_ov',1:10,'_est'))
# stored "true" values of parameters by each condition
ptvec <- c(rep('lambdaT',11), 'psiW12T', 'psiB1T', 'psiB2T', 'psiB12T', rep("thetaBT", 10), rep('icc_lv',2), rep('icc_ov',10))
result <- read_csv(paste0(w.d, "/data/results_bias_est.csv"))Parsed with column specification:
cols(
N1 = col_double(),
N2 = col_double(),
ICC_LV = col_double(),
ICC_OV = col_double(),
Variable = col_character(),
Estimator = col_character(),
TrueValue = col_double(),
RB = col_double(),
RMSE = col_double(),
Bias = col_double(),
SampVar = col_double(),
muRE = col_double(),
mwRE = col_double(),
uwRE = col_double(),
nRep = col_double(),
estMean = col_double(),
estSD = col_double()
)# Set conditions levels as categorical values
result <- result %>%
mutate(N1 = factor(N1, c("5", "10", "30")),
N2 = factor(N2, c("30", "50", "100", "200")),
ICC_OV = factor(ICC_OV, c("0.1","0.3", "0.5")),
ICC_LV = factor(ICC_LV, c("0.1", "0.5")),
wi = nRep/500)Summarizing Results
First, we will plot estimates (botxplots) to show how these estimates changed across conditions. To summarize the results we will average over the parameters that only differ y indices. Meaning we will describe the “average factor loading bias” by reporting the average bias for factor loadings. Additionally, different conditions resultedin different “sample sizes.” By this we mean the number of uses replications. The different number of cases per condition was accounted for by creating a “weight” variable for each row of the result object. This meant that conditions that had more usable replications counted more towards to averages reported (or count as much as if we averaged over the individual replications).
Factor loadings
sdat <- filter(result, Variable %like% 'lambda')
# first, plot estimates
p1 <- ggplot(sdat, aes(y=estMean))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading")
p2 <- ggplot(sdat, aes(y=estSD))+
geom_boxplot()+
labs(y="SD of Factor Loadings")
p3 <- ggplot(sdat, aes(y=RB))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias")
p4 <- ggplot(sdat, aes(y=RMSE))+
geom_boxplot()+
labs(y="Root Mean Square Error")
p5 <- ggplot(sdat, aes(y=Bias))+
geom_boxplot()+
labs(y="Sqaured Bias")
p6 <- ggplot(sdat, aes(y=SampVar))+
geom_boxplot()+
labs(y="Sampling Variance of Estimates")
p <- (p1 + p2 + p3)/(p4 + p5 + p6) +
plot_annotation(title="Summarizing bias indices of FACTOR LOADINGS")
p
Single Condition Breakdown
Estimation Method
ggplot(sdat, aes(y=estMean))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading",
title="FACTOR LOADINGS by Estimation Method",
subtitle="Parameter Estimates")+
facet_wrap(.~Estimator)
ggplot(sdat, aes(y=estSD))+
geom_boxplot()+
labs(y="SD of Factor Loadings",
title="FACTOR LOADINGS by Estimation Method",
subtitle="Standard Deviation of Estimates")+
facet_wrap(.~Estimator)
ggplot(sdat, aes(y=RB))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias",
title="FACTOR LOADINGS by Estimation Method",
subtitle="Relative Bias of Estimates")+
facet_wrap(.~Estimator)
ggplot(sdat, aes(y=RMSE))+
geom_boxplot()+
labs(y="Root Mean Square Error",
title="FACTOR LOADINGS by Estimation Method",
subtitle="Root Mean Square Error of Estimates")+
facet_wrap(.~Estimator)
ggplot(sdat, aes(y=Bias))+
geom_boxplot()+
labs(y="Sqaured Bias",
title="FACTOR LOADINGS by Estimation Method",
subtitle="Squared Bias of Estiamtes")+
facet_wrap(.~Estimator)
ggplot(sdat, aes(y=SampVar))+
geom_boxplot()+
labs(y="Sampling Variance",
title="FACTOR LOADINGS by Estimation Method",
subtitle="Sampling Variance of Estimates")+
facet_wrap(.~Estimator)
c <- sdat %>%
group_by(Estimator) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi),
Bias = weighted.mean(Bias, wi),
SampVar =weighted.mean(SampVar, wi))
kable(c, format='html', digits=3,
caption="Summary Indices of FACTOR LOADINGS by Estimation Method") %>%
kable_styling(full_width = T)| Estimator | est | RB | RMSE | Bias | SampVar |
|---|---|---|---|---|---|
| MLR | 0.391 | -34.881 | 0.048 | 0.046 | 0.002 |
| ULSMV | 0.592 | -1.317 | 0.026 | 0.000 | 0.025 |
| WLSMV | 0.605 | 0.809 | 0.007 | 0.000 | 0.007 |
Level-2 Sample Size
ggplot(sdat, aes(y=estMean))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading",
title="FACTOR LOADINGS by Level-2 Sample Size",
subtitle="Parameter Estimates")+
facet_wrap(.~N2)
ggplot(sdat, aes(y=estSD))+
geom_boxplot()+
labs(y="SD of Factor Loadings",
title="FACTOR LOADINGS by Level-2 Sample Size",
subtitle="Standard Deviation of Parameter Estimates")+
facet_wrap(.~N2)
ggplot(sdat, aes(y=RB))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias",
title="FACTOR LOADINGS by Level-2 Sample Size",
subtitle="Relative Bias Parameter Estimates")+
facet_wrap(.~N2)
ggplot(sdat, aes(y=RMSE))+
geom_boxplot()+
labs(y="Root Mean Square Error",
title="FACTOR LOADINGS by Level-2 Sample Size",
subtitle="Root Mean Square Error")+
facet_wrap(.~N2)
ggplot(sdat, aes(y=Bias))+
geom_boxplot()+
labs(y="Sqaured Bias",
title="FACTOR LOADINGS by Level-2 Sample Size",
subtitle="Squared Bias of Parameter Estimates")+
facet_wrap(.~N2)
ggplot(sdat, aes(y=SampVar))+
geom_boxplot()+
labs(y="Sampling Variance of Estimates",
title="FACTOR LOADINGS by Level-2 Sample Size",
subtitle="Sampling Variance of Parameter Estimates")+
facet_wrap(.~N2)
c <- sdat %>%
group_by(N2) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi),
Bias = weighted.mean(Bias, wi),
SampVar =weighted.mean(SampVar, wi))
kable(c, format='html', digits=3,
caption="Summary Indices of FACTOR LOADINGS by Level-2 Sample Size") %>%
kable_styling(full_width = T)| N2 | est | RB | RMSE | Bias | SampVar |
|---|---|---|---|---|---|
| 30 | 0.516 | -14.0 | 0.044 | 0.019 | 0.025 |
| 50 | 0.521 | -13.2 | 0.031 | 0.017 | 0.013 |
| 100 | 0.526 | -12.4 | 0.023 | 0.016 | 0.007 |
| 200 | 0.529 | -11.9 | 0.019 | 0.015 | 0.004 |
Level-1 Sample Size
ggplot(sdat, aes(y=estMean))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading",
title="FACTOR LOADINGS by Level-1",
subtitle="Parameter Estimates")+
facet_wrap(.~N1)
ggplot(sdat, aes(y=estSD))+
geom_boxplot()+
labs(y="SD of Factor Loadings",
title="FACTOR LOADINGS by Level-1 Sample Size",
subtitle="Standard Deviation of Parameter Estimates")+
facet_wrap(.~N1)
ggplot(sdat, aes(y=RB))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias",
title="FACTOR LOADINGS by Level-1 Sample Size",
subtitle="Relative Bias of Parameter Estimates")+
facet_wrap(.~N1)
ggplot(sdat, aes(y=RMSE))+
geom_boxplot()+
labs(y="Root Mean Square Error",
title="FACTOR LOADINGS by Level-1 Sample Size",
subtitle="Root Mean Square Error")+
facet_wrap(.~N1)
ggplot(sdat, aes(y=Bias))+
geom_boxplot()+
labs(y="Sqaured Bias",
title="FACTOR LOADINGS by Level-1 Sample Size",
subtitle="Squared Bias of Parameter Estimates")+
facet_wrap(.~N1)
ggplot(sdat, aes(y=SampVar))+
geom_boxplot()+
labs(y="Sampling Variance of Estimates",
title="FACTOR LOADINGS by Level-1 Sample Size",
subtitle="Sampling Variance of Parameter Estimates")+
facet_wrap(.~N1)
c <- sdat %>%
group_by(N1) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi),
Bias = weighted.mean(Bias, wi),
SampVar =weighted.mean(SampVar, wi))
kable(c, format='html', digits=3,
caption="Summary Indices of FACTOR LOADINGS by Level-1 Sample Size") %>%
kable_styling(full_width = T)| N1 | est | RB | RMSE | Bias | SampVar |
|---|---|---|---|---|---|
| 5 | 0.520 | -13.4 | 0.037 | 0.018 | 0.019 |
| 10 | 0.524 | -12.7 | 0.026 | 0.017 | 0.010 |
| 30 | 0.526 | -12.3 | 0.022 | 0.016 | 0.006 |
ICC Observed Variables
ggplot(sdat, aes(y=estMean))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading",
title="FACTOR LOADINGS by ICC of Observed Variables",
subtitle="Parameter Estimates")+
facet_wrap(.~ICC_OV)
ggplot(sdat, aes(y=estSD))+
geom_boxplot()+
labs(y="SD of Factor Loadings",
title="FACTOR LOADINGS by ICC of Observed Variables",
subtitle="Standard Deviation of Parameter Estimates")+
facet_wrap(.~ICC_OV)
ggplot(sdat, aes(y=RB))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias",
title="FACTOR LOADINGS by ICC of Observed Variables",
subtitle="Relative Bias of Parameter Estimates")+
facet_wrap(.~ICC_OV)
ggplot(sdat, aes(y=RMSE))+
geom_boxplot()+
labs(y="Root Mean Square Error",
title="FACTOR LOADINGS by ICC of Observed Variables",
subtitle="Root Mean Square Error of Parameter Estimates")+
facet_wrap(.~ICC_OV)
ggplot(sdat, aes(y=Bias))+
geom_boxplot()+
labs(y="Sqaured Bias",
title="FACTOR LOADINGS by ICC of Observed Variables",
subtitle="Squared Bias of Parameter Estimates")+
facet_wrap(.~ICC_OV)
ggplot(sdat, aes(y=SampVar))+
geom_boxplot()+
labs(y="Sampling Variance of Estimates",
title="FACTOR LOADINGS by ICC of Observed Variables",
subtitle="Sampling Variance of Parameter Estimates")+
facet_wrap(.~ICC_OV)
c <- sdat %>%
group_by(ICC_OV) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi),
Bias = weighted.mean(Bias, wi),
SampVar =weighted.mean(SampVar, wi))
kable(c, format='html', digits=3, caption="Summary Indices of FACTOR LOADINGS by ICC of Observed Variables") %>%
kable_styling(full_width = T)| ICC_OV | est | RB | RMSE | Bias | SampVar |
|---|---|---|---|---|---|
| 0.1 | 0.532 | -11.3 | 0.015 | 0.012 | 0.003 |
| 0.3 | 0.531 | -11.4 | 0.021 | 0.015 | 0.006 |
| 0.5 | 0.508 | -15.3 | 0.045 | 0.022 | 0.023 |
ICC Latent Variables
ggplot(sdat, aes(y=estMean))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading",
title="FACTOR LOADINGS by ICC of Latent Variables",
subtitle="Parameter Estimates")+
facet_wrap(.~ICC_LV)
ggplot(sdat, aes(y=estSD))+
geom_boxplot()+
labs(y="SD of Factor Loadings",
title="FACTOR LOADINGS by ICC of Latent Variables",
subtitle="Standard Deviation of Parameter Estimates")+
facet_wrap(.~ICC_LV)
ggplot(sdat, aes(y=RB))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias",
title="FACTOR LOADINGS by ICC of Latent Variables",
subtitle="Relative Bias of Parameter Estimates")+
facet_wrap(.~ICC_LV)
ggplot(sdat, aes(y=RMSE))+
geom_boxplot()+
labs(y="Root Mean Square Error",
title="FACTOR LOADINGS by ICC of Latent Variables",
subtitle="Root Mean Square Error of Parameter Estimates")+
facet_wrap(.~ICC_LV)
ggplot(sdat, aes(y=Bias))+
geom_boxplot()+
labs(y="Sqaured Bias",
title="FACTOR LOADINGS by ICC of Latent Variables",
subtitle="Squared Bias of Parameter Estimates")+
facet_wrap(.~ICC_LV)
ggplot(sdat, aes(y=SampVar))+
geom_boxplot()+
labs(y="Sampling Variance of Estimates",
title="FACTOR LOADINGS by ICC of Latent Variables",
subtitle="Sampling Variance of Parameter Estimates")+
facet_wrap(.~ICC_LV)
c <- sdat %>%
group_by(ICC_LV) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi),
Bias = weighted.mean(Bias, wi),
SampVar =weighted.mean(SampVar, wi))
kable(c, format='html', digits=3,
caption="Summary Indices of FACTOR LOADINGS by ICC of Latent Variables") %>%
kable_styling(full_width = T)| ICC_LV | est | RB | RMSE | Bias | SampVar |
|---|---|---|---|---|---|
| 0.1 | 0.535 | -10.8 | 0.019 | 0.013 | 0.006 |
| 0.5 | 0.514 | -14.3 | 0.035 | 0.020 | 0.015 |
Loadings by Estimation Method and Sample Sizes
Estimation Method & Level-2 Sample Size
ggplot(sdat, aes(y=estMean))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading")+
facet_grid(N2~Estimator)
ggplot(sdat, aes(y=RB))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias")+
facet_grid(N2~Estimator)
ggplot(sdat, aes(y=RMSE))+
geom_boxplot()+
labs(y="Root Mean Square Error")+
facet_grid(N2~Estimator)
c <- sdat %>%
group_by(Estimator, N2) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi),
Bias = weighted.mean(Bias, wi),
SampVar =weighted.mean(SampVar, wi))
c1 <- cbind(c[ c$Estimator == 'MLR', c( 'N2', 'est', 'RB', 'RMSE')],
c[ c$Estimator == 'ULSMV', c('est', 'RB', 'RMSE')],
c[ c$Estimator == 'WLSMV', c('est', 'RB', 'RMSE')])
colnames(c1) <- c('N2', rep(c('est', 'RB', 'RMSE'), 3))
kable(c1, format='html', digits=3, row.names = F) %>%
kable_styling(full_width = T) %>%
add_header_above(c(' '=1, 'MLR'=3, 'ULSMV'=3, 'WLSMV'=3))| N2 | est | RB | RMSE | est | RB | RMSE | est | RB | RMSE |
|---|---|---|---|---|---|---|---|---|---|
| 30 | 0.387 | -35.4 | 0.051 | 0.588 | -2.076 | 0.061 | 0.611 | 1.770 | 0.017 |
| 50 | 0.390 | -35.1 | 0.049 | 0.589 | -1.812 | 0.030 | 0.606 | 1.040 | 0.009 |
| 100 | 0.392 | -34.6 | 0.046 | 0.593 | -1.094 | 0.016 | 0.604 | 0.606 | 0.004 |
| 200 | 0.393 | -34.5 | 0.046 | 0.596 | -0.674 | 0.009 | 0.602 | 0.283 | 0.002 |
Estimation Method & Level-1 Sample Size
ggplot(sdat, aes(y=estMean))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading")+
facet_grid(N1~Estimator)
ggplot(sdat, aes(y=RB))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias")+
facet_grid(N1~Estimator)
ggplot(sdat, aes(y=RMSE))+
geom_boxplot()+
labs(y="Root Mean Square Error")+
facet_grid(N1~Estimator)
c <- sdat %>%
group_by(Estimator, N1) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi),
Bias = weighted.mean(Bias, wi),
SampVar =weighted.mean(SampVar, wi))
c1 <- cbind(c[ c$Estimator == 'MLR', c( 'N1', 'est', 'RB', 'RMSE')],
c[ c$Estimator == 'ULSMV', c('est', 'RB', 'RMSE')],
c[ c$Estimator == 'WLSMV', c('est', 'RB', 'RMSE')])
colnames(c1) <- c('N1', rep(c('est', 'RB', 'RMSE'), 3))
kable(c1, format='html', digits=3, row.names = F) %>%
kable_styling(full_width = T) %>%
add_header_above(c(' '=1, 'MLR'=3, 'ULSMV'=3, 'WLSMV'=3))| N1 | est | RB | RMSE | est | RB | RMSE | est | RB | RMSE |
|---|---|---|---|---|---|---|---|---|---|
| 5 | 0.387 | -35.4 | 0.051 | 0.592 | -1.27 | 0.043 | 0.610 | 1.656 | 0.014 |
| 10 | 0.391 | -34.9 | 0.048 | 0.592 | -1.31 | 0.022 | 0.605 | 0.784 | 0.006 |
| 30 | 0.393 | -34.5 | 0.045 | 0.592 | -1.36 | 0.016 | 0.601 | 0.248 | 0.002 |
Estimation Method, Level-2 Sample Size & Level-1 Sample Size
ggplot(sdat, aes(y=estMean,x=N1, group=N1))+
geom_boxplot()+
geom_hline(yintercept = 0.6, color="red")+
labs(y="Average Factor Loading")+
facet_grid(N2~Estimator)
ggplot(sdat, aes(y=RB,x=N1, group=N1))+
geom_boxplot()+
geom_hline(yintercept=-10, color="red", linetype="dashed")+
geom_hline(yintercept=10, color="red", linetype="dashed")+
labs(y="Relative Bias")+
facet_grid(N2~Estimator)
ggplot(sdat, aes(y=RMSE,x=N1, group=N1))+
geom_boxplot()+
labs(y="Root Mean Square Error")+
facet_grid(N2~Estimator)
c <- sdat %>%
group_by(Estimator, N2, N1) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi),
Bias = weighted.mean(Bias, wi),
SampVar =weighted.mean(SampVar, wi))
c1 <- cbind(c[ c$Estimator == 'MLR', c( 'N2','N1', 'est', 'RB', 'RMSE')],
c[ c$Estimator == 'ULSMV', c('est', 'RB', 'RMSE')],
c[ c$Estimator == 'WLSMV', c('est', 'RB', 'RMSE')])
colnames(c1) <- c('N2','N1', rep(c('est', 'RB', 'RMSE'), 3))
kable(c1, format='html', digits=3, row.names = F) %>%
kable_styling(full_width = T) %>%
add_header_above(c(' '=2, 'MLR'=3, 'ULSMV'=3, 'WLSMV'=3))| N2 | N1 | est | RB | RMSE | est | RB | RMSE | est | RB | RMSE |
|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 5 | 0.382 | -36.3 | 0.057 | 0.584 | -2.604 | 0.145 | 0.626 | 4.321 | 0.040 |
| 30 | 10 | 0.385 | -35.8 | 0.052 | 0.591 | -1.562 | 0.043 | 0.610 | 1.727 | 0.016 |
| 30 | 30 | 0.393 | -34.5 | 0.046 | 0.587 | -2.184 | 0.030 | 0.603 | 0.486 | 0.005 |
| 50 | 5 | 0.384 | -36.0 | 0.053 | 0.588 | -2.012 | 0.048 | 0.615 | 2.440 | 0.020 |
| 50 | 10 | 0.390 | -35.0 | 0.048 | 0.586 | -2.378 | 0.033 | 0.606 | 0.979 | 0.009 |
| 50 | 30 | 0.393 | -34.4 | 0.046 | 0.593 | -1.235 | 0.017 | 0.602 | 0.285 | 0.003 |
| 100 | 5 | 0.389 | -35.1 | 0.049 | 0.595 | -0.768 | 0.022 | 0.607 | 1.199 | 0.009 |
| 100 | 10 | 0.393 | -34.5 | 0.046 | 0.593 | -1.226 | 0.017 | 0.604 | 0.605 | 0.004 |
| 100 | 30 | 0.394 | -34.4 | 0.045 | 0.593 | -1.211 | 0.012 | 0.601 | 0.174 | 0.001 |
| 200 | 5 | 0.392 | -34.7 | 0.046 | 0.596 | -0.593 | 0.011 | 0.603 | 0.465 | 0.004 |
| 200 | 10 | 0.393 | -34.5 | 0.045 | 0.597 | -0.440 | 0.007 | 0.602 | 0.291 | 0.002 |
| 200 | 30 | 0.393 | -34.5 | 0.045 | 0.594 | -0.977 | 0.009 | 0.601 | 0.117 | 0.001 |
Relative Efficiency by Sample Sizes
c <- sdat %>%
group_by(Estimator, N2, N1) %>%
summarise(mu = weighted.mean(muRE, wi),
mw = weighted.mean(mwRE, wi),
uw = weighted.mean(uwRE, wi))
c1 <- cbind(c[ c$Estimator == 'MLR', c( 'N2','N1', 'mu', 'mw', 'uw')])
colnames(c1) <- c('N2','N1',c('MLR/ULSMV', 'MLR/WLSMV', 'ULSMV/WLSMV'))
kable(c1, format='html', digits=3, row.names = F) %>%
kable_styling(full_width = T)| N2 | N1 | MLR/ULSMV | MLR/WLSMV | ULSMV/WLSMV |
|---|---|---|---|---|
| 30 | 5 | 1.84 | 2.18 | 1.57 |
| 30 | 10 | 1.68 | 2.33 | 1.54 |
| 30 | 30 | 2.27 | 3.75 | 2.04 |
| 50 | 5 | 1.81 | 2.37 | 1.44 |
| 50 | 10 | 1.78 | 2.81 | 1.78 |
| 50 | 30 | 2.84 | 4.27 | 1.95 |
| 100 | 5 | 2.02 | 2.71 | 1.46 |
| 100 | 10 | 2.79 | 3.64 | 1.63 |
| 100 | 30 | 4.03 | 5.90 | 2.17 |
| 200 | 5 | 2.91 | 3.53 | 1.40 |
| 200 | 10 | 3.86 | 4.86 | 1.54 |
| 200 | 30 | 5.92 | 8.24 | 2.39 |
Relative Efficiency by All Conditions
c <- sdat %>%
group_by(Estimator, N2, N1, ICC_OV, ICC_LV) %>%
summarise(mu = weighted.mean(muRE, wi),
mw = weighted.mean(mwRE, wi),
uw = weighted.mean(uwRE, wi))
c1 <- cbind(c[ c$Estimator == 'MLR', c( 'N2','N1','ICC_OV', 'ICC_LV', 'mu', 'mw', 'uw')])
colnames(c1) <- c('N2','N1', 'ICC_OV', 'ICC_LV',c('MLR/ULSMV', 'MLR/WLSMV', 'ULSMV/WLSMV'))
kable(c1, format='html', digits=3, row.names = F) %>%
kable_styling(full_width = T)| N2 | N1 | ICC_OV | ICC_LV | MLR/ULSMV | MLR/WLSMV | ULSMV/WLSMV |
|---|---|---|---|---|---|---|
| 30 | 5 | 0.1 | 0.1 | 4.164 | 3.60 | 0.871 |
| 30 | 5 | 0.1 | 0.5 | 4.773 | 5.39 | 1.178 |
| 30 | 5 | 0.3 | 0.1 | 1.336 | 1.56 | 1.171 |
| 30 | 5 | 0.3 | 0.5 | 1.147 | 1.39 | 1.229 |
| 30 | 5 | 0.5 | 0.1 | 0.757 | 1.47 | 2.035 |
| 30 | 5 | 0.5 | 0.5 | 0.631 | 1.37 | 2.425 |
| 30 | 10 | 0.1 | 0.1 | 1.930 | 1.80 | 0.937 |
| 30 | 10 | 0.1 | 0.5 | 3.696 | 4.71 | 1.275 |
| 30 | 10 | 0.3 | 0.1 | 1.618 | 1.84 | 1.142 |
| 30 | 10 | 0.3 | 0.5 | 1.471 | 2.04 | 1.397 |
| 30 | 10 | 0.5 | 0.1 | 1.478 | 2.11 | 1.459 |
| 30 | 10 | 0.5 | 0.5 | 0.865 | 2.12 | 2.466 |
| 30 | 30 | 0.1 | 0.1 | 2.365 | 2.35 | 0.997 |
| 30 | 30 | 0.1 | 0.5 | 4.033 | 6.62 | 1.641 |
| 30 | 30 | 0.3 | 0.1 | 2.779 | 2.94 | 1.058 |
| 30 | 30 | 0.3 | 0.5 | 1.968 | 3.37 | 1.722 |
| 30 | 30 | 0.5 | 0.1 | 1.445 | 3.33 | 2.344 |
| 30 | 30 | 0.5 | 0.5 | 0.836 | 3.52 | 4.266 |
| 50 | 5 | 0.1 | 0.1 | 2.461 | 2.27 | 0.923 |
| 50 | 5 | 0.1 | 0.5 | 4.235 | 5.61 | 1.328 |
| 50 | 5 | 0.3 | 0.1 | 1.456 | 1.68 | 1.157 |
| 50 | 5 | 0.3 | 0.5 | 1.505 | 1.86 | 1.241 |
| 50 | 5 | 0.5 | 0.1 | 1.537 | 1.96 | 1.282 |
| 50 | 5 | 0.5 | 0.5 | 0.845 | 1.86 | 2.216 |
| 50 | 10 | 0.1 | 0.1 | 1.956 | 1.87 | 0.956 |
| 50 | 10 | 0.1 | 0.5 | 2.325 | 4.80 | 2.072 |
| 50 | 10 | 0.3 | 0.1 | 2.090 | 2.19 | 1.049 |
| 50 | 10 | 0.3 | 0.5 | 2.005 | 2.59 | 1.294 |
| 50 | 10 | 0.5 | 0.1 | 1.655 | 2.66 | 1.657 |
| 50 | 10 | 0.5 | 0.5 | 0.843 | 2.78 | 3.324 |
| 50 | 30 | 0.1 | 0.1 | 2.987 | 2.98 | 0.997 |
| 50 | 30 | 0.1 | 0.5 | 4.102 | 5.47 | 1.337 |
| 50 | 30 | 0.3 | 0.1 | 3.603 | 3.71 | 1.030 |
| 50 | 30 | 0.3 | 0.5 | 2.251 | 4.35 | 1.941 |
| 50 | 30 | 0.5 | 0.1 | 3.470 | 4.46 | 1.287 |
| 50 | 30 | 0.5 | 0.5 | 1.011 | 4.65 | 4.662 |
| 100 | 5 | 0.1 | 0.1 | 2.041 | 1.95 | 0.954 |
| 100 | 5 | 0.1 | 0.5 | 2.468 | 4.38 | 1.779 |
| 100 | 5 | 0.3 | 0.1 | 2.021 | 2.11 | 1.046 |
| 100 | 5 | 0.3 | 0.5 | 2.379 | 2.59 | 1.088 |
| 100 | 5 | 0.5 | 0.1 | 2.328 | 2.54 | 1.094 |
| 100 | 5 | 0.5 | 0.5 | 1.130 | 2.74 | 2.490 |
| 100 | 10 | 0.1 | 0.1 | 2.446 | 2.46 | 1.006 |
| 100 | 10 | 0.1 | 0.5 | 4.118 | 4.96 | 1.206 |
| 100 | 10 | 0.3 | 0.1 | 2.950 | 3.02 | 1.023 |
| 100 | 10 | 0.3 | 0.5 | 3.067 | 3.71 | 1.213 |
| 100 | 10 | 0.5 | 0.1 | 3.359 | 3.62 | 1.078 |
| 100 | 10 | 0.5 | 0.5 | 1.017 | 3.99 | 3.977 |
| 100 | 30 | 0.1 | 0.1 | 4.227 | 4.24 | 1.003 |
| 100 | 30 | 0.1 | 0.5 | 5.436 | 6.56 | 1.209 |
| 100 | 30 | 0.3 | 0.1 | 5.174 | 5.23 | 1.011 |
| 100 | 30 | 0.3 | 0.5 | 3.219 | 6.27 | 1.996 |
| 100 | 30 | 0.5 | 0.1 | 5.502 | 6.47 | 1.178 |
| 100 | 30 | 0.5 | 0.5 | 1.095 | 6.76 | 6.283 |
| 200 | 5 | 0.1 | 0.1 | 2.391 | 2.43 | 1.015 |
| 200 | 5 | 0.1 | 0.5 | 4.291 | 4.62 | 1.078 |
| 200 | 5 | 0.3 | 0.1 | 2.785 | 2.82 | 1.012 |
| 200 | 5 | 0.3 | 0.5 | 3.408 | 3.77 | 1.105 |
| 200 | 5 | 0.5 | 0.1 | 3.323 | 3.43 | 1.033 |
| 200 | 5 | 0.5 | 0.5 | 1.369 | 4.02 | 2.970 |
| 200 | 10 | 0.1 | 0.1 | 3.380 | 3.43 | 1.016 |
| 200 | 10 | 0.1 | 0.5 | 5.046 | 5.38 | 1.069 |
| 200 | 10 | 0.3 | 0.1 | 4.208 | 4.22 | 1.003 |
| 200 | 10 | 0.3 | 0.5 | 4.266 | 5.26 | 1.235 |
| 200 | 10 | 0.5 | 0.1 | 4.966 | 5.16 | 1.039 |
| 200 | 10 | 0.5 | 0.5 | 1.545 | 5.77 | 3.760 |
| 200 | 30 | 0.1 | 0.1 | 5.955 | 5.98 | 1.005 |
| 200 | 30 | 0.1 | 0.5 | 7.547 | 8.26 | 1.096 |
| 200 | 30 | 0.3 | 0.1 | 7.362 | 7.45 | 1.012 |
| 200 | 30 | 0.3 | 0.5 | 5.367 | 8.96 | 1.673 |
| 200 | 30 | 0.5 | 0.1 | 8.453 | 9.04 | 1.070 |
| 200 | 30 | 0.5 | 0.5 | 1.205 | 9.86 | 8.267 |
Manuscript Table
c <- sdat %>%
group_by(Estimator, N2, N1) %>%
summarise(est = weighted.mean(estMean, wi),
RB = weighted.mean(RB, wi),
RMSE = weighted.mean(RMSE, wi))
c1 <- cbind(c[ c$Estimator == 'MLR', c( 'N2','N1', 'est', 'RB', 'RMSE')],
c[ c$Estimator == 'ULSMV', c('est', 'RB', 'RMSE')],
c[ c$Estimator == 'WLSMV', c('est', 'RB', 'RMSE')])
colnames(c1) <- c('N2','N1', rep(c('est', 'RB', 'RMSE'), 3))
kable(c1, format='html', digits=3, row.names = F) %>%
kable_styling(full_width = T) %>%
add_header_above(c(' '=2, 'MLR'=3, 'ULSMV'=3, 'WLSMV'=3))| N2 | N1 | est | RB | RMSE | est | RB | RMSE | est | RB | RMSE |
|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 5 | 0.382 | -36.3 | 0.057 | 0.584 | -2.604 | 0.145 | 0.626 | 4.321 | 0.040 |
| 30 | 10 | 0.385 | -35.8 | 0.052 | 0.591 | -1.562 | 0.043 | 0.610 | 1.727 | 0.016 |
| 30 | 30 | 0.393 | -34.5 | 0.046 | 0.587 | -2.184 | 0.030 | 0.603 | 0.486 | 0.005 |
| 50 | 5 | 0.384 | -36.0 | 0.053 | 0.588 | -2.012 | 0.048 | 0.615 | 2.440 | 0.020 |
| 50 | 10 | 0.390 | -35.0 | 0.048 | 0.586 | -2.378 | 0.033 | 0.606 | 0.979 | 0.009 |
| 50 | 30 | 0.393 | -34.4 | 0.046 | 0.593 | -1.235 | 0.017 | 0.602 | 0.285 | 0.003 |
| 100 | 5 | 0.389 | -35.1 | 0.049 | 0.595 | -0.768 | 0.022 | 0.607 | 1.199 | 0.009 |
| 100 | 10 | 0.393 | -34.5 | 0.046 | 0.593 | -1.226 | 0.017 | 0.604 | 0.605 | 0.004 |
| 100 | 30 | 0.394 | -34.4 | 0.045 | 0.593 | -1.211 | 0.012 | 0.601 | 0.174 | 0.001 |
| 200 | 5 | 0.392 | -34.7 | 0.046 | 0.596 | -0.593 | 0.011 | 0.603 | 0.465 | 0.004 |
| 200 | 10 | 0.393 | -34.5 | 0.045 | 0.597 | -0.440 | 0.007 | 0.602 | 0.291 | 0.002 |
| 200 | 30 | 0.393 | -34.5 | 0.045 | 0.594 | -0.977 | 0.009 | 0.601 | 0.117 | 0.001 |
print(xtable(c1, digits = 3,align=c("l", "l", "l", rep("r",9)),
display=c("s", "d","d", rep("f",9)),
caption="Mean Factor Loading, Relative Bias, and RMSE by Estimation Method",
label="tb:fct"),
booktabs = T, include.rownames = F,
caption.placement = "top")% latex table generated in R 3.6.3 by xtable 1.8-4 package
% Wed Jun 10 21:19:03 2020
\begin{table}[ht]
\centering
\caption{Mean Factor Loading, Relative Bias, and RMSE by Estimation Method}
\label{tb:fct}
\begin{tabular}{llrrrrrrrrr}
\toprule
N2 & N1 & est & RB & RMSE & est & RB & RMSE & est & RB & RMSE \\
\midrule
30 & 5 & 0.382 & -36.268 & 0.057 & 0.584 & -2.604 & 0.145 & 0.626 & 4.321 & 0.040 \\
30 & 10 & 0.385 & -35.848 & 0.052 & 0.591 & -1.562 & 0.043 & 0.610 & 1.727 & 0.016 \\
30 & 30 & 0.393 & -34.520 & 0.046 & 0.587 & -2.184 & 0.030 & 0.603 & 0.486 & 0.005 \\
50 & 5 & 0.384 & -36.029 & 0.053 & 0.588 & -2.012 & 0.048 & 0.615 & 2.440 & 0.020 \\
50 & 10 & 0.390 & -34.951 & 0.048 & 0.586 & -2.378 & 0.033 & 0.606 & 0.979 & 0.009 \\
50 & 30 & 0.393 & -34.433 & 0.046 & 0.593 & -1.235 & 0.017 & 0.602 & 0.285 & 0.003 \\
100 & 5 & 0.389 & -35.139 & 0.049 & 0.595 & -0.768 & 0.022 & 0.607 & 1.199 & 0.009 \\
100 & 10 & 0.393 & -34.481 & 0.046 & 0.593 & -1.226 & 0.017 & 0.604 & 0.605 & 0.004 \\
100 & 30 & 0.394 & -34.390 & 0.045 & 0.593 & -1.211 & 0.012 & 0.601 & 0.174 & 0.001 \\
200 & 5 & 0.392 & -34.684 & 0.046 & 0.596 & -0.593 & 0.011 & 0.603 & 0.465 & 0.004 \\
200 & 10 & 0.393 & -34.467 & 0.045 & 0.597 & -0.440 & 0.007 & 0.602 & 0.291 & 0.002 \\
200 & 30 & 0.393 & -34.489 & 0.045 & 0.594 & -0.977 & 0.009 & 0.601 & 0.117 & 0.001 \\
\bottomrule
\end{tabular}
\end{table}
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
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] xtable_1.8-4 kableExtra_1.1.0 cowplot_1.0.0
[4] MplusAutomation_0.7-3 data.table_1.12.8 patchwork_1.0.0
[7] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[10] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[13] tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0
[16] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.1 jsonlite_1.6.1 viridisLite_0.3.0 gsubfn_0.7
[5] modelr_0.1.8 assertthat_0.2.1 highr_0.8 pander_0.6.3
[9] blob_1.2.1 cellranger_1.1.0 yaml_2.2.1 pillar_1.4.4
[13] backports_1.1.7 lattice_0.20-38 glue_1.4.1 digest_0.6.25
[17] promises_1.1.0 rvest_0.3.5 colorspace_1.4-1 htmltools_0.4.0
[21] httpuv_1.5.2 plyr_1.8.6 pkgconfig_2.0.3 broom_0.5.6
[25] haven_2.3.0 scales_1.1.1 webshot_0.5.2 whisker_0.4
[29] later_1.0.0 git2r_0.27.1 farver_2.0.3 generics_0.0.2
[33] ellipsis_0.3.1 withr_2.2.0 cli_2.0.2 proto_1.0.0
[37] magrittr_1.5 crayon_1.3.4 readxl_1.3.1 evaluate_0.14
[41] fs_1.4.1 fansi_0.4.1 nlme_3.1-144 xml2_1.3.2
[45] tools_3.6.3 hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
[49] reprex_0.3.0 compiler_3.6.3 rlang_0.4.6 grid_3.6.3
[53] rstudioapi_0.11 texreg_1.36.23 labeling_0.3 rmarkdown_2.1
[57] boot_1.3-24 gtable_0.3.0 DBI_1.1.0 R6_2.4.1
[61] lubridate_1.7.8 knitr_1.28 rprojroot_1.3-2 stringi_1.4.6
[65] parallel_3.6.3 Rcpp_1.0.4.6 vctrs_0.3.0 dbplyr_1.4.4
[69] tidyselect_1.1.0 xfun_0.14 coda_0.19-3