Average Relative Difference Among Parameter Estimates: Level-1 Factor Covariance
2020-06-10
Last updated: 2020-06-10
Checks: 6 1
Knit directory: mcfa-para-est/
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The purpose of this page is the identify the degree of separation between estimates of parameters between estimation methods.
Packages and Set-Up
rm(list=ls())
source(paste0(getwd(),"/code/load_packages.R"))
source(paste0(getwd(),"/code/get_data.R"))
source(paste0(getwd(),"/code/r_functions.R"))
# general options
theme_set(theme_bw())
options(digits=3)
##Chunk iptions
knitr::opts_chunk$set(out.width="225%")Data Management
# set up vectors of variable names
pvec <- c(paste0('lambda1',1:6), paste0('lambda2',6:10), 'psiW12','psiB1', 'psiB2', 'psiB12', paste0('thetaB',1:10), 'icc_lv1_est', 'icc_lv2_est', paste0('icc_ov',1:10,'_est'))
# stored "true" values of parameters by each condition
ptvec <- c(rep('lambdaT',11), 'psiW12T', 'psiB1T', 'psiB2T', 'psiB12T', rep("thetaBT", 10), rep('icc_lv',2), rep('icc_ov',10))
# take out non-converged/inadmissible cases
sim_results <- filter(sim_results, Converge==1, Admissible==1)
# Set conditions levels as categorical values
sim_results <- sim_results %>%
mutate(N1 = factor(N1, c("5", "10", "30")),
N2 = factor(N2, c("30", "50", "100", "200")),
ICC_OV = factor(ICC_OV, c("0.1","0.3", "0.5")),
ICC_LV = factor(ICC_LV, c("0.1", "0.5")))
# convert to long format
long_res1 <- sim_results[,c("Condition", "Replication", "N1", "N2", "ICC_OV", "ICC_LV", "Estimator", pvec)] %>%
pivot_longer(
cols = all_of(pvec),
names_to = "Parameter",
values_to = "Estimate"
)
long_res2 <- tibble(sim_results[,c("Condition", "Replication", "N1", "N2", "ICC_OV", "ICC_LV", "Estimator", ptvec)], .name_repair="universal")
ptvec <- colnames(long_res2)[8:44]
long_res2 <- long_res2 %>%
pivot_longer(
cols = all_of(ptvec),
names_to = "ParameterT",
values_to = "Truth"
)
long_results <- long_res1
long_results$ParameterT <- long_res2$ParameterT
long_results$Truth <- long_res2$Truth
# Now, make "wider"
wide_res <- long_results %>%
pivot_wider(
names_from = "Estimator",
values_from = "Estimate"
)
# Subset to just factor loadings
wide_res <- filter(wide_res, Parameter %like% "psiW")Estimate Relative Difference
wide_res <- wide_res %>%
mutate(
MLR_ULSMV = ((MLR - ULSMV))/Truth*100,
MLR_WLSMV = ((MLR - WLSMV))/Truth*100,
ULSMV_WLSMV = ((ULSMV - WLSMV))/Truth*100
)
long_results <- wide_res %>%
pivot_longer(
cols= all_of(c("MLR_ULSMV", "MLR_WLSMV", "ULSMV_WLSMV")),
names_to = "Comparison",
values_to = "ARD"
)
ard.est <- long_results %>%
group_by(Condition, N1, N2, ICC_OV, ICC_LV, Comparison) %>%
summarise(
ARD = mean(ARD, na.rm=T)
)
ard.est$Comparison <- factor(ard.est$Comparison,
levels=c("MLR_ULSMV", "MLR_WLSMV", "ULSMV_WLSMV"),
labels=c("MLR/ULSMV)",
"MLR/WLSMV)",
"ULSMV/WLSMV)"),
ordered=T)Single Conditions
All Conditions
p <- ggplot(ard.est, aes(x=Comparison, y=ARD)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width=0.3) +
geom_hline(yintercept = 10, color="red")+
geom_hline(yintercept = -10, color="red")+
scale_y_continuous(limits=c(-100, 100), breaks=c(-100, -60, -30, -10, 0, 10, 30, 60, 100))+
labs(y="Average Relative Difference",
x=NULL,
title="ARD: Level-1 Factor Covariance")+
theme_bw()+
theme(panel.grid = element_blank())
p
By Level-1 Sample Size
p <- ggplot(ard.est, aes(x=Comparison, y=ARD)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width=0.3) +
geom_hline(yintercept = 10, color="red")+
geom_hline(yintercept = -10, color="red")+
scale_y_continuous(limits=c(-100, 100), breaks=c(-100, -60, -30, -10, 0, 10, 30, 60, 100))+
labs(y="Average Relative Difference",
x=NULL,
title="ARD: Level-1 Factor Covariance",
subtitle = "Conditional on Level-1 Sample Size")+
#scale_color_manual(name="% Admissible", values=cols)+
facet_wrap(.~N1)+
theme_bw()+
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle=90))
p
By Level-2 Sample Size
p <- ggplot(ard.est, aes(x=Comparison, y=ARD)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width=0.3) +
geom_hline(yintercept = 10, color="red")+
geom_hline(yintercept = -10, color="red")+
scale_y_continuous(limits=c(-100, 100), breaks=c(-100, -60, -30, -10, 0, 10, 30, 60, 100))+
labs(y="Average Relative Difference",
x=NULL,
title="ARD: Level-1 Factor Covariance",
subtitle = "Conditional on Level-2 Sample Size")+
#scale_color_manual(name="% Admissible", values=cols)+
facet_wrap(.~N2, nrow=1)+
theme_bw()+
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle=90))
p
By Observed Variable ICC
p <- ggplot(ard.est, aes(x=Comparison, y=ARD)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width=0.3) +
geom_hline(yintercept = 10, color="red")+
geom_hline(yintercept = -10, color="red")+
scale_y_continuous(limits=c(-100, 100), breaks=c(-100, -60, -30, -10, 0, 10, 30, 60, 100))+
labs(y="Average Relative Difference",
x=NULL,
title="ARD: Level-1 Factor Covariance",
subtitle = "Conditional on Observed Variable ICC")+
#scale_color_manual(name="% Admissible", values=cols)+
facet_wrap(.~ICC_OV)+
theme_bw()+
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle=90))
p
By Latent Variable ICC
p <- ggplot(ard.est, aes(x=Comparison, y=ARD)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width=0.3) +
geom_hline(yintercept = 10, color="red")+
geom_hline(yintercept = -10, color="red")+
scale_y_continuous(limits=c(-100, 100), breaks=c(-100, -60, -30, -10, 0, 10, 30, 60, 100))+
labs(y="Average Relative Difference",
x=NULL,
title="ARD: Level-1 Factor Covariance",
subtitle = "Conditional on Latent Variable ICC")+
#scale_color_manual(name="% Admissible", values=cols)+
facet_wrap(.~ICC_LV)+
theme_bw()+
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle=90))
p
Publication Figure
p <- ggplot(ard.est, aes(x=Comparison, y=ARD)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width=0.3, size=2, alpha=0.7) +
geom_hline(yintercept = 10, color="red")+
geom_hline(yintercept = -10, color="red")+
scale_y_continuous(limits=c(-100, 100), breaks=c(-100, -60, -30, -10, 0, 10, 30, 60, 100))+
labs(y="Average Relative Difference",
x=NULL)+
theme_bw()+
theme(panel.grid = element_blank(),
legend.position = c(0.5,0.25),
legend.text = element_text(size=12),
legend.title = element_text(size=12),
axis.title.y = element_text(size=12),
axis.text.y = element_text(size=10),
axis.text.x = element_text(angle=20, hjust=1,
vjust = 1, size=12))
p
#ggsave("fig/estimate_correlation_factor_loading.pdf",p, units="in", height=3.5, width=5)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 pander_0.6.3 blob_1.2.1
[9] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.4 backports_1.1.7
[13] lattice_0.20-38 glue_1.4.1 digest_0.6.25 promises_1.1.0
[17] rvest_0.3.5 colorspace_1.4-1 htmltools_0.4.0 httpuv_1.5.2
[21] plyr_1.8.6 pkgconfig_2.0.3 broom_0.5.6 haven_2.3.0
[25] scales_1.1.1 webshot_0.5.2 later_1.0.0 git2r_0.27.1
[29] generics_0.0.2 farver_2.0.3 ellipsis_0.3.1 withr_2.2.0
[33] cli_2.0.2 proto_1.0.0 magrittr_1.5 crayon_1.3.4
[37] readxl_1.3.1 evaluate_0.14 fs_1.4.1 fansi_0.4.1
[41] nlme_3.1-144 xml2_1.3.2 tools_3.6.3 hms_0.5.3
[45] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 compiler_3.6.3
[49] rlang_0.4.6 grid_3.6.3 rstudioapi_0.11 texreg_1.36.23
[53] rmarkdown_2.1 boot_1.3-24 gtable_0.3.0 DBI_1.1.0
[57] R6_2.4.1 lubridate_1.7.8 knitr_1.28 rprojroot_1.3-2
[61] stringi_1.4.6 parallel_3.6.3 Rcpp_1.0.4.6 vctrs_0.3.0
[65] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.14 coda_0.19-3
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 pander_0.6.3 blob_1.2.1
[9] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.4 backports_1.1.7
[13] lattice_0.20-38 glue_1.4.1 digest_0.6.25 promises_1.1.0
[17] rvest_0.3.5 colorspace_1.4-1 htmltools_0.4.0 httpuv_1.5.2
[21] plyr_1.8.6 pkgconfig_2.0.3 broom_0.5.6 haven_2.3.0
[25] scales_1.1.1 webshot_0.5.2 later_1.0.0 git2r_0.27.1
[29] generics_0.0.2 farver_2.0.3 ellipsis_0.3.1 withr_2.2.0
[33] cli_2.0.2 proto_1.0.0 magrittr_1.5 crayon_1.3.4
[37] readxl_1.3.1 evaluate_0.14 fs_1.4.1 fansi_0.4.1
[41] nlme_3.1-144 xml2_1.3.2 tools_3.6.3 hms_0.5.3
[45] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 compiler_3.6.3
[49] rlang_0.4.6 grid_3.6.3 rstudioapi_0.11 texreg_1.36.23
[53] rmarkdown_2.1 boot_1.3-24 gtable_0.3.0 DBI_1.1.0
[57] R6_2.4.1 lubridate_1.7.8 knitr_1.28 rprojroot_1.3-2
[61] stringi_1.4.6 parallel_3.6.3 Rcpp_1.0.4.6 vctrs_0.3.0
[65] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.14 coda_0.19-3