ML-CFA: Convergence of Parameter Estimates
Last updated: 2020-03-30
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Knit directory: mcfa-para-est/
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rm(list=ls())
source(paste0(getwd(),"/code/load_packages.R"))
source(paste0(getwd(),"/code/get_data.R"))sessionInfo()R version 3.6.1 (2019-07-05)
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 MplusAutomation_0.7-3
[4] data.table_1.12.6 forcats_0.4.0 stringr_1.4.0
[7] dplyr_0.8.3 purrr_0.3.3 readr_1.3.1
[10] tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
[13] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 lubridate_1.7.4 lattice_0.20-38 assertthat_0.2.1
[5] zeallot_0.1.0 rprojroot_1.3-2 digest_0.6.23 R6_2.4.1
[9] cellranger_1.1.0 plyr_1.8.4 backports_1.1.5 reprex_0.3.0
[13] evaluate_0.14 coda_0.19-3 httr_1.4.1 pillar_1.4.2
[17] rlang_0.4.2 lazyeval_0.2.2 readxl_1.3.1 rstudioapi_0.10
[21] texreg_1.36.23 rmarkdown_1.18 gsubfn_0.7 proto_1.0.0
[25] webshot_0.5.2 pander_0.6.3 munsell_0.5.0 broom_0.5.2
[29] compiler_3.6.1 httpuv_1.5.2 modelr_0.1.5 xfun_0.11
[33] pkgconfig_2.0.3 htmltools_0.4.0 tidyselect_0.2.5 workflowr_1.5.0
[37] viridisLite_0.3.0 crayon_1.3.4 dbplyr_1.4.2 withr_2.1.2
[41] later_1.0.0 grid_3.6.1 nlme_3.1-140 jsonlite_1.6
[45] gtable_0.3.0 lifecycle_0.1.0 DBI_1.0.0 git2r_0.26.1
[49] magrittr_1.5 scales_1.1.0 cli_1.1.0 stringi_1.4.3
[53] fs_1.3.1 promises_1.1.0 xml2_1.2.2 generics_0.0.2
[57] vctrs_0.2.0 boot_1.3-22 tools_3.6.1 glue_1.3.1
[61] hms_0.5.2 parallel_3.6.1 yaml_2.2.0 colorspace_1.4-1
[65] rvest_0.3.5 knitr_1.26 haven_2.2.0 Trace Plots
sim_results <- filter(sim_results, Converge==1 & Admissible==1)Factor Covariance
# Small N
con_dat <- filter(sim_results, ss_l2==30, ss_l1==10,
icc_lv==0.1, icc_ov==0.3)
p <- ggplot(con_dat, aes(x=Replication, y=psiB12,
group=Estimator, color=Estimator)) +
geom_line() +
labs(y='Level-2 Factor Covariance')+
theme_bw()
p
ggsave("manuscript/fig/fact-cov-converge-smallN.pdf", plot=p, width=7, height=4, units="in")
# Medium N
con_dat <- filter(sim_results, ss_l2==100, ss_l1==10,
icc_lv==0.1, icc_ov==0.3)
p <- ggplot(con_dat, aes(x=Replication, y=psiB12,
group=Estimator, color=Estimator)) +
geom_line() +
labs(y='Level-2 Factor Covariance')+
theme_bw()
p
ggsave("manuscript/fig/fact-cov-converge-medN.pdf", plot=p, width=7, height=4, units="in")
## Large N
con_dat <- filter(sim_results, ss_l2==200, ss_l1==10,
icc_lv==0.1, icc_ov==0.3)
p <- ggplot(con_dat, aes(x=Replication, y=psiB12,
group=Estimator, color=Estimator)) +
geom_line() +
labs(y='Level-2 Factor Covariance')+
theme_bw()
p
ggsave("manuscript/fig/fact-cov-converge-largeN.pdf", plot=p, width=7, height=4, units="in")Factor Covariance
# Small N
con_dat <- filter(sim_results, ss_l2==30, ss_l1==10,
icc_lv==0.1, icc_ov==0.3)
p <- ggplot(con_dat, aes(x=Replication, y=psiB1,
group=Estimator, color=Estimator)) +
geom_line() +
geom_hline(yintercept = 0.11, linetype="dashed")+
labs(y='Level-2 Factor Variance')+
theme_bw()
p
ggsave("manuscript/fig/fact-var-converge-smallN.pdf", plot=p, width=7, height=4, units="in")
## Large N
con_dat <- filter(sim_results, ss_l2==200, ss_l1==10,
icc_lv==0.1, icc_ov==0.3)
p <- ggplot(con_dat, aes(x=Replication, y=psiB1,
group=Estimator, color=Estimator)) +
geom_line() +
geom_hline(yintercept = 0.11, linetype="dashed")+
labs(y='Level-2 Factor Variance')+
theme_bw()
p
ggsave("manuscript/fig/fact-var-converge-largeN.pdf", plot=p, width=7, height=4, units="in")Item 1 Loading
# Small N
con_dat <- filter(sim_results, ss_l2==30, ss_l1==10,
icc_lv==0.1, icc_ov==0.3)
p <- ggplot(con_dat, aes(x=Replication, y=lambda11,
group=Estimator, color=Estimator)) +
geom_line() +
geom_hline(yintercept = 0.6, linetype="dashed")+
labs(y='Factor Loading (Item 1)')+
theme_bw()
p
ggsave("manuscript/fig/loading-converge-smallN.pdf", plot=p, width=7, height=4, units="in")
# Medium N
con_dat <- filter(sim_results, ss_l2==100, ss_l1==10,
icc_lv==0.1, icc_ov==0.3)
p <- ggplot(con_dat, aes(x=Replication, y=lambda11,
group=Estimator, color=Estimator)) +
geom_line() +
geom_hline(yintercept = 0.6, linetype="dashed")+
labs(y='Factor Loading (Item 1)')+
theme_bw()
p
ggsave("manuscript/fig/loading-converge-medN.pdf", plot=p, width=7, height=4, units="in")
## Large N
con_dat <- filter(sim_results, ss_l2==200, ss_l1==10,
icc_lv==0.1, icc_ov==0.3)
p <- ggplot(con_dat, aes(x=Replication, y=lambda11,
group=Estimator, color=Estimator)) +
geom_line() +
geom_hline(yintercept = 0.6, linetype="dashed")+
labs(y='Factor Loading (Item 1)')+
theme_bw()
p
ggsave("manuscript/fig/loading-converge-largeN.pdf", plot=p, width=7, height=4, units="in")
sessionInfo()R version 3.6.1 (2019-07-05)
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 MplusAutomation_0.7-3
[4] data.table_1.12.6 forcats_0.4.0 stringr_1.4.0
[7] dplyr_0.8.3 purrr_0.3.3 readr_1.3.1
[10] tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
[13] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 lubridate_1.7.4 lattice_0.20-38 assertthat_0.2.1
[5] zeallot_0.1.0 rprojroot_1.3-2 digest_0.6.23 R6_2.4.1
[9] cellranger_1.1.0 plyr_1.8.4 backports_1.1.5 reprex_0.3.0
[13] evaluate_0.14 coda_0.19-3 httr_1.4.1 pillar_1.4.2
[17] rlang_0.4.2 lazyeval_0.2.2 readxl_1.3.1 rstudioapi_0.10
[21] texreg_1.36.23 rmarkdown_1.18 gsubfn_0.7 labeling_0.3
[25] proto_1.0.0 webshot_0.5.2 pander_0.6.3 munsell_0.5.0
[29] broom_0.5.2 compiler_3.6.1 httpuv_1.5.2 modelr_0.1.5
[33] xfun_0.11 pkgconfig_2.0.3 htmltools_0.4.0 tidyselect_0.2.5
[37] workflowr_1.5.0 viridisLite_0.3.0 crayon_1.3.4 dbplyr_1.4.2
[41] withr_2.1.2 later_1.0.0 grid_3.6.1 nlme_3.1-140
[45] jsonlite_1.6 gtable_0.3.0 lifecycle_0.1.0 DBI_1.0.0
[49] git2r_0.26.1 magrittr_1.5 scales_1.1.0 cli_1.1.0
[53] stringi_1.4.3 farver_2.0.1 fs_1.3.1 promises_1.1.0
[57] xml2_1.2.2 generics_0.0.2 vctrs_0.2.0 boot_1.3-22
[61] tools_3.6.1 glue_1.3.1 hms_0.5.2 parallel_3.6.1
[65] yaml_2.2.0 colorspace_1.4-1 rvest_0.3.5 knitr_1.26
[69] haven_2.2.0