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rm(list=ls())
source(paste0(getwd(),"/code/load_packages.R"))
source(paste0(getwd(),"/code/get_data.R"))

sessionInfo()

R version 4.0.0 (2020-04-24)
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 datasets  utils     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_1.0.0          
[10] purrr_0.3.4           readr_1.3.1           tidyr_1.1.0          
[13] tibble_3.0.1          ggplot2_3.3.1         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] renv_0.10.0       cellranger_1.1.0  yaml_2.2.1        pillar_1.4.4     
[13] backports_1.1.7   lattice_0.20-41   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_1.5.3.1    plyr_1.8.6        pkgconfig_2.0.3   broom_0.5.6      
[25] haven_2.3.1       scales_1.1.1      webshot_0.5.2     whisker_0.4      
[29] later_1.0.0       git2r_0.27.1      generics_0.0.2    ellipsis_0.3.1   
[33] withr_2.2.0       cli_2.0.2         proto_1.0.0       magrittr_1.5     
[37] crayon_1.3.4      readxl_1.3.1      evaluate_0.14     fs_1.4.1         
[41] fansi_0.4.1       nlme_3.1-147      xml2_1.3.2        tools_4.0.0      
[45] hms_0.5.3         lifecycle_0.2.0   munsell_0.5.0     reprex_0.3.0     
[49] compiler_4.0.0    rlang_0.4.6       grid_4.0.0        rstudioapi_0.11  
[53] texreg_1.37.1     rmarkdown_2.2     boot_1.3-24       gtable_0.3.0     
[57] DBI_1.1.0         R6_2.4.1          lubridate_1.7.8   knitr_1.28       
[61] rprojroot_1.3-2   stringi_1.4.6     parallel_4.0.0    Rcpp_1.0.4.6     
[65] vctrs_0.3.0       dbplyr_1.4.4      tidyselect_1.1.0  xfun_0.14        
[69] coda_0.19-3

Convergence

Model converge is when …

Single Condition Breakdown

Estimation Method

c <- sim_results %>%
  group_by(Estimator) %>%
  summarise(Converge=mean(Converge))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("Estimation Method", "Convergence")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

Estimation Method	Convergence
MLR	0.9998
ULSMV	0.9990
WLSMV	0.9998

Level-2 Sample Size

c <- sim_results %>%
  group_by(ss_l2) %>%
  summarise(Converge=mean(Converge))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("Level-2 N", "Convergence")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

Level-2 N	Convergence
30	0.9984
50	0.9999
100	0.9999
200	0.9999

Level-1 Sample Size

c <- sim_results %>%
  group_by(ss_l1) %>%
  summarise(Converge=mean(Converge))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("Level-1 N", "Convergence")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

Level-1 N	Convergence
5	0.9989
10	0.9998
30	0.9998

ICC Observed Variables

c <- sim_results %>%
  group_by(icc_ov) %>%
  summarise(Converge=mean(Converge))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("ICC Observed Variable", "Convergence")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

ICC Observed Variable	Convergence
0.1	0.9998
0.3	0.9998
0.5	0.9989

ICC Latent Variables

c <- sim_results %>%
  group_by(icc_lv) %>%
  summarise(Converge=mean(Converge))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("ICC Latent Variable", "Convergence")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

ICC Latent Variable	Convergence
0.1	0.9995
0.5	0.9995

Convergence by Estimation Method and Sample Sizes

Estimation Method & Level-2 Sample Size

c <- sim_results %>%
  group_by(Estimator, ss_l2) %>%
  summarise(Converge=mean(Converge))

`summarise()` regrouping output by 'Estimator' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c( 'ss_l2', 'Converge')], 
           c[ c$Estimator == 'ULSMV', 'Converge'], 
           c[ c$Estimator == 'WLSMV', 'Converge'])
colnames(c1) <- c('Level-2 N', 'MLR', 'ULSMV', 'WLSMV')


kable(c1, format='html', digits=4) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 1, 'Estimation Method'=3))

	Estimation Method
Level-2 N	MLR	ULSMV	WLSMV
30	1.0000	0.9961	0.9992
50	0.9999	0.9998	0.9999
100	0.9998	1.0000	1.0000
200	0.9996	1.0000	1.0000

Estimation Method & Level-1 Sample Size

c <- sim_results %>%
  group_by(Estimator, ss_l1) %>%
  summarise(Converge=mean(Converge))

`summarise()` regrouping output by 'Estimator' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c( 'ss_l1', 'Converge')], 
           c[ c$Estimator == 'ULSMV', 'Converge'], 
           c[ c$Estimator == 'WLSMV', 'Converge'])
colnames(c1) <- c('Level-1 N', 'MLR', 'ULSMV', 'WLSMV')


kable(c1, format='html', digits=4) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 1, 'Estimation Method'=3))

	Estimation Method
Level-1 N	MLR	ULSMV	WLSMV
5	0.9999	0.9973	0.9994
10	0.9999	0.9997	0.9999
30	0.9996	0.9999	1.0000

Estimation Method, Level-2 Sample Size & Level-1 Sample Size

c <- sim_results %>%
  group_by(Estimator, ss_l1, ss_l2) %>%
  summarise(Converge=mean(Converge))

`summarise()` regrouping output by 'Estimator', 'ss_l1' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c( 'ss_l1', 'ss_l2', 'Converge')], 
           c[ c$Estimator == 'ULSMV', 'Converge'], 
           c[ c$Estimator == 'WLSMV', 'Converge'])

New names:
* Converge -> Converge...3
* Converge -> Converge...4
* Converge -> Converge...5

colnames(c1) <- c('Level-1 N', 'Level-2 N', 'MLR', 'ULSMV', 'WLSMV')


kable(c1, format='html', digits=4) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 2, 'Estimation Method'=3))

		Estimation Method
Level-1 N	Level-2 N	MLR	ULSMV	WLSMV
5	30	1.0000	0.9897	0.9977
5	50	1.0000	0.9997	1.0000
5	100	1.0000	1.0000	1.0000
5	200	0.9997	1.0000	1.0000
10	30	1.0000	0.9990	1.0000
10	50	1.0000	0.9997	0.9997
10	100	1.0000	1.0000	1.0000
10	200	0.9997	1.0000	1.0000
30	30	1.0000	0.9997	1.0000
30	50	0.9997	1.0000	1.0000
30	100	0.9993	1.0000	1.0000
30	200	0.9993	1.0000	1.0000

Across all conditions

c <- sim_results %>%
  group_by(Estimator, ss_l1, ss_l2, icc_ov, icc_lv) %>%
  summarise(Converge=mean(Converge))

`summarise()` regrouping output by 'Estimator', 'ss_l1', 'ss_l2', 'icc_ov' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c('ss_l2', 'ss_l1', 'icc_ov', 'icc_lv', 'Converge')],
            c[ c$Estimator == 'ULSMV', 'Converge'],
            c[ c$Estimator == 'WLSMV', 'Converge'])

New names:
* Converge -> Converge...5
* Converge -> Converge...6
* Converge -> Converge...7

colnames(c1) <- c('Level-2 N', 'Level-1 N', 'ICC-OV', 'ICC-LV', 'MLR', 'ULSMV', 'WLSMV')


kable(c1, format='html', digits=4) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 4, 'Estimation Method'=3))

				Estimation Method
Level-2 N	Level-1 N	ICC-OV	ICC-LV	MLR	ULSMV	WLSMV
30	5	0.1	0.1	1.000	0.998	0.992
30	5	0.1	0.5	1.000	1.000	1.000
30	5	0.3	0.1	1.000	0.998	1.000
30	5	0.3	0.5	1.000	1.000	1.000
30	5	0.5	0.1	1.000	0.966	0.998
30	5	0.5	0.5	1.000	0.976	0.996
50	5	0.1	0.1	1.000	1.000	1.000
50	5	0.1	0.5	1.000	1.000	1.000
50	5	0.3	0.1	1.000	1.000	1.000
50	5	0.3	0.5	1.000	1.000	1.000
50	5	0.5	0.1	1.000	0.998	1.000
50	5	0.5	0.5	1.000	1.000	1.000
100	5	0.1	0.1	1.000	1.000	1.000
100	5	0.1	0.5	1.000	1.000	1.000
100	5	0.3	0.1	1.000	1.000	1.000
100	5	0.3	0.5	1.000	1.000	1.000
100	5	0.5	0.1	1.000	1.000	1.000
100	5	0.5	0.5	1.000	1.000	1.000
200	5	0.1	0.1	1.000	1.000	1.000
200	5	0.1	0.5	1.000	1.000	1.000
200	5	0.3	0.1	1.000	1.000	1.000
200	5	0.3	0.5	1.000	1.000	1.000
200	5	0.5	0.1	1.000	1.000	1.000
200	5	0.5	0.5	0.998	1.000	1.000
30	10	0.1	0.1	1.000	1.000	1.000
30	10	0.1	0.5	1.000	1.000	1.000
30	10	0.3	0.1	1.000	1.000	1.000
30	10	0.3	0.5	1.000	1.000	1.000
30	10	0.5	0.1	1.000	0.998	1.000
30	10	0.5	0.5	1.000	0.996	1.000
50	10	0.1	0.1	1.000	1.000	1.000
50	10	0.1	0.5	1.000	0.998	0.998
50	10	0.3	0.1	1.000	1.000	1.000
50	10	0.3	0.5	1.000	1.000	1.000
50	10	0.5	0.1	1.000	1.000	1.000
50	10	0.5	0.5	1.000	1.000	1.000
100	10	0.1	0.1	1.000	1.000	1.000
100	10	0.1	0.5	1.000	1.000	1.000
100	10	0.3	0.1	1.000	1.000	1.000
100	10	0.3	0.5	1.000	1.000	1.000
100	10	0.5	0.1	1.000	1.000	1.000
100	10	0.5	0.5	1.000	1.000	1.000
200	10	0.1	0.1	1.000	1.000	1.000
200	10	0.1	0.5	1.000	1.000	1.000
200	10	0.3	0.1	0.998	1.000	1.000
200	10	0.3	0.5	1.000	1.000	1.000
200	10	0.5	0.1	1.000	1.000	1.000
200	10	0.5	0.5	1.000	1.000	1.000
30	30	0.1	0.1	1.000	1.000	1.000
30	30	0.1	0.5	1.000	1.000	1.000
30	30	0.3	0.1	1.000	1.000	1.000
30	30	0.3	0.5	1.000	1.000	1.000
30	30	0.5	0.1	1.000	1.000	1.000
30	30	0.5	0.5	1.000	0.998	1.000
50	30	0.1	0.1	1.000	1.000	1.000
50	30	0.1	0.5	1.000	1.000	1.000
50	30	0.3	0.1	1.000	1.000	1.000
50	30	0.3	0.5	0.998	1.000	1.000
50	30	0.5	0.1	1.000	1.000	1.000
50	30	0.5	0.5	1.000	1.000	1.000
100	30	0.1	0.1	1.000	1.000	1.000
100	30	0.1	0.5	1.000	1.000	1.000
100	30	0.3	0.1	1.000	1.000	1.000
100	30	0.3	0.5	0.996	1.000	1.000
100	30	0.5	0.1	1.000	1.000	1.000
100	30	0.5	0.5	1.000	1.000	1.000
200	30	0.1	0.1	1.000	1.000	1.000
200	30	0.1	0.5	1.000	1.000	1.000
200	30	0.3	0.1	1.000	1.000	1.000
200	30	0.3	0.5	0.996	1.000	1.000
200	30	0.5	0.1	1.000	1.000	1.000
200	30	0.5	0.5	1.000	1.000	1.000

Admissibility Summary

Admissibility rates are first subsetted to the converged models. So, the rates may seem misleading and not directly relatable across all conditions and models due to differences in convergence rates.

c.sim_results <- filter(sim_results, Converge == 1)

Single Condition Breakdown

Estimation Method

c <- c.sim_results %>%
  group_by(Estimator) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("Estimation Method", "Admissible")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

Estimation Method	Admissible
MLR	0.8340
ULSMV	0.7651
WLSMV	0.7217

Level-2 Sample Size

c <- c.sim_results %>%
  group_by(ss_l2) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("Level-2 N", "Admissible")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

Level-2 N	Admissible
30	0.5993
50	0.7160
100	0.8424
200	0.9364

Level-1 Sample Size

c <- c.sim_results %>%
  group_by(ss_l1) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("Level-1 N", "Admissible")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

Level-1 N	Admissible
5	0.6524
10	0.7837
30	0.8846

ICC Observed Variables

c <- c.sim_results %>%
  group_by(icc_ov) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("ICC Observed Variable", "Admissible")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

ICC Observed Variable	Admissible
0.1	0.6774
0.3	0.8554
0.5	0.7881

ICC Latent Variables

c <- c.sim_results %>%
  group_by(icc_lv) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` ungrouping output (override with `.groups` argument)

colnames(c)<- c("ICC Latent Variable", "Admissible")

kable(c, format='html', digits=4) %>%
    kable_styling(full_width = T)

ICC Latent Variable	Admissible
0.1	0.6953
0.5	0.8520

Admissible Replications by Estimation Method and Sample Sizes

Estimation Method & Level-2 Sample Size

c <- c.sim_results %>%
  group_by(Estimator, ss_l2) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` regrouping output by 'Estimator' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c( 'ss_l2', 'Admissible')], 
           c[ c$Estimator == 'ULSMV', 'Admissible'], 
           c[ c$Estimator == 'WLSMV', 'Admissible'])
colnames(c1) <- c('Level-2 N', 'MLR', 'ULSMV', 'WLSMV')

kable(c1, format='html', digits=4) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 1, 'Estimation Method'=3))

	Estimation Method
Level-2 N	MLR	ULSMV	WLSMV
30	0.6994	0.5809	0.5175
50	0.7916	0.7064	0.6501
100	0.8906	0.8366	0.8000
200	0.9543	0.9358	0.9192

Estimation Method & Level-1 Sample Size

c <- c.sim_results %>%
  group_by(Estimator, ss_l1) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` regrouping output by 'Estimator' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c( 'ss_l1', 'Admissible')], 
           c[ c$Estimator == 'ULSMV', 'Admissible'], 
           c[ c$Estimator == 'WLSMV', 'Admissible'])
colnames(c1) <- c('Level-1 N', 'MLR', 'ULSMV', 'WLSMV')

kable(c1, format='html', digits=4) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 1, 'Estimation Method'=3))

	Estimation Method
Level-1 N	MLR	ULSMV	WLSMV
5	0.7455	0.6267	0.5849
10	0.8409	0.7780	0.7321
30	0.9156	0.8902	0.8481

Estimation Method, Level-2 Sample Size & Level-1 Sample Size

c <- c.sim_results %>%
  group_by(Estimator, ss_l1, ss_l2) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` regrouping output by 'Estimator', 'ss_l1' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c( 'ss_l1', 'ss_l2', 'Admissible')], 
           c[ c$Estimator == 'ULSMV', 'Admissible'], 
           c[ c$Estimator == 'WLSMV', 'Admissible'])

New names:
* Admissible -> Admissible...3
* Admissible -> Admissible...4
* Admissible -> Admissible...5

colnames(c1) <- c('Level-1 N', 'Level-2 N', 'MLR', 'ULSMV', 'WLSMV')

kable(c1, format='html', digits=4) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 2, 'Estimation Method'=3))

		Estimation Method
Level-1 N	Level-2 N	MLR	ULSMV	WLSMV
5	30	0.5753	0.4052	0.3558
5	50	0.6773	0.5318	0.4700
5	100	0.8057	0.6920	0.6647
5	200	0.9236	0.8753	0.8487
10	30	0.6847	0.5736	0.5080
10	50	0.7957	0.7076	0.6606
10	100	0.9200	0.8730	0.8243
10	200	0.9633	0.9577	0.9357
30	30	0.8383	0.7623	0.6883
30	50	0.9020	0.8797	0.8197
30	100	0.9463	0.9447	0.9110
30	200	0.9760	0.9743	0.9733

Admissible Replications for individual conditions

c <- c.sim_results %>%
  group_by(Estimator, ss_l1, ss_l2, icc_ov, icc_lv) %>%
  summarise(Admissible=mean(Admissible))

`summarise()` regrouping output by 'Estimator', 'ss_l1', 'ss_l2', 'icc_ov' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c('ss_l2', 'ss_l1', 'icc_ov', 'icc_lv', 'Admissible')],
            c[ c$Estimator == 'ULSMV', 'Admissible'],
            c[ c$Estimator == 'WLSMV', 'Admissible'])

New names:
* Admissible -> Admissible...5
* Admissible -> Admissible...6
* Admissible -> Admissible...7

colnames(c1) <- c('Level-2 N', 'Level-1 N', 'ICC-OV', 'ICC-LV', 'MLR', 'ULSMV', 'WLSMV')


kable(c1, format='html', digits=4) %>%
  kable_styling(full_width = T) %>%
  add_header_above(c(' '= 4, 'Estimation Method'=3))

				Estimation Method
Level-2 N	Level-1 N	ICC-OV	ICC-LV	MLR	ULSMV	WLSMV
30	5	0.1	0.1	0.522	0.0301	0.0484
30	5	0.1	0.5	0.372	0.0360	0.0240
30	5	0.3	0.1	0.468	0.3567	0.2500
30	5	0.3	0.5	0.792	0.7300	0.6740
30	5	0.5	0.1	0.360	0.3354	0.2244
30	5	0.5	0.5	0.938	0.9529	0.9137
50	5	0.1	0.1	0.560	0.1380	0.1460
50	5	0.1	0.5	0.500	0.0920	0.0520
50	5	0.3	0.1	0.548	0.5420	0.4040
50	5	0.3	0.5	0.972	0.9380	0.8680
50	5	0.5	0.1	0.488	0.4830	0.3580
50	5	0.5	0.5	0.996	0.9980	0.9920
100	5	0.1	0.1	0.752	0.4940	0.5560
100	5	0.1	0.5	0.726	0.3140	0.2380
100	5	0.3	0.1	0.760	0.7580	0.6820
100	5	0.3	0.5	0.996	0.9900	0.9820
100	5	0.5	0.1	0.600	0.5960	0.5320
100	5	0.5	0.5	1.000	1.0000	0.9980
200	5	0.1	0.1	0.952	0.8900	0.9000
200	5	0.1	0.5	0.956	0.7280	0.6140
200	5	0.3	0.1	0.890	0.8800	0.8620
200	5	0.3	0.5	1.000	1.0000	1.0000
200	5	0.5	0.1	0.744	0.7540	0.7160
200	5	0.5	0.5	1.000	1.0000	1.0000
30	10	0.1	0.1	0.608	0.3520	0.3640
30	10	0.1	0.5	0.494	0.1340	0.0800
30	10	0.3	0.1	0.584	0.5740	0.4180
30	10	0.3	0.5	0.988	0.9520	0.8540
30	10	0.5	0.1	0.446	0.4489	0.3580
30	10	0.5	0.5	0.988	0.9819	0.9740
50	10	0.1	0.1	0.832	0.6680	0.7260
50	10	0.1	0.5	0.762	0.3868	0.2184
50	10	0.3	0.1	0.698	0.6980	0.6180
50	10	0.3	0.5	1.000	0.9940	0.9760
50	10	0.5	0.1	0.482	0.4980	0.4240
50	10	0.5	0.5	1.000	1.0000	1.0000
100	10	0.1	0.1	0.970	0.9320	0.9460
100	10	0.1	0.5	0.988	0.7540	0.5180
100	10	0.3	0.1	0.890	0.8840	0.8460
100	10	0.3	0.5	1.000	1.0000	0.9980
100	10	0.5	0.1	0.672	0.6680	0.6380
100	10	0.5	0.5	1.000	1.0000	1.0000
200	10	0.1	0.1	1.000	0.9980	1.0000
200	10	0.1	0.5	1.000	0.9780	0.8560
200	10	0.3	0.1	0.984	0.9800	0.9800
200	10	0.3	0.5	1.000	1.0000	1.0000
200	10	0.5	0.1	0.796	0.7900	0.7780
200	10	0.5	0.5	1.000	1.0000	1.0000
30	30	0.1	0.1	0.950	0.9020	0.8900
30	30	0.1	0.5	0.938	0.5180	0.2340
30	30	0.3	0.1	0.688	0.6900	0.6360
30	30	0.3	0.5	1.000	0.9900	0.9500
30	30	0.5	0.1	0.460	0.4820	0.4320
30	30	0.5	0.5	0.994	0.9920	0.9880
50	30	0.1	0.1	0.996	0.9880	0.9900
50	30	0.1	0.5	0.994	0.8440	0.5520
50	30	0.3	0.1	0.830	0.8480	0.8160
50	30	0.3	0.5	1.000	1.0000	1.0000
50	30	0.5	0.1	0.592	0.5980	0.5600
50	30	0.5	0.5	1.000	1.0000	1.0000
100	30	0.1	0.1	1.000	1.0000	1.0000
100	30	0.1	0.5	1.000	0.9700	0.8120
100	30	0.3	0.1	0.962	0.9600	0.9620
100	30	0.3	0.5	1.000	1.0000	1.0000
100	30	0.5	0.1	0.716	0.7380	0.6920
100	30	0.5	0.5	1.000	1.0000	1.0000
200	30	0.1	0.1	1.000	1.0000	1.0000
200	30	0.1	0.5	1.000	1.0000	0.9820
200	30	0.3	0.1	0.998	1.0000	0.9980
200	30	0.3	0.5	1.000	1.0000	1.0000
200	30	0.5	0.1	0.858	0.8460	0.8600
200	30	0.5	0.5	1.000	1.0000	1.0000

Manuscript Table(s)

c <- sim_results %>%
  group_by(Estimator, ss_l2) %>%
  mutate(A = ifelse(is.na(Admissible), 0, Admissible)) %>%
  summarise(Admissible=mean(A, na.rm = T))

`summarise()` regrouping output by 'Estimator' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c( 'ss_l2','Admissible')], 
           c[ c$Estimator == 'ULSMV', 'Admissible'], 
           c[ c$Estimator == 'WLSMV', 'Admissible'])
colnames(c1) <- c('Level-2 N_2', 'MLR', 'ULSMV', 'WLSMV')


kable(c1, format='html', digits=3) %>%
  kable_styling(full_width = T)

Level-2 N_2	MLR	ULSMV	WLSMV
30	0.699	0.579	0.517
50	0.792	0.706	0.650
100	0.890	0.837	0.800
200	0.954	0.936	0.919

print(xtable(c1, digits = 3,align=c("l", "l", "r", "r", "r"),
             display=c("s", "d", "f", "f", "f"),
             caption="Rates of admissible replications",
             label="tb:admiss"),
      booktabs = T, include.rownames = F,
      caption.placement = "top")

% latex table generated in R 4.0.0 by xtable 1.8-4 package
% Fri Jun 05 10:47:52 2020
\begin{table}[ht]
\centering
\caption{Rates of admissible replications} 
\label{tb:admiss}
\begin{tabular}{lrrr}
  \toprule
Level-2 N\_2 & MLR & ULSMV & WLSMV \\ 
  \midrule
  30 & 0.699 & 0.579 & 0.517 \\ 
    50 & 0.792 & 0.706 & 0.650 \\ 
   100 & 0.890 & 0.837 & 0.800 \\ 
   200 & 0.954 & 0.936 & 0.919 \\ 
   \bottomrule
\end{tabular}
\end{table}

# Version 2
c <- sim_results %>%
  group_by(Estimator, ss_l2, ss_l1) %>%
  mutate(A = ifelse(is.na(Admissible), 0, Admissible)) %>%
  summarise(Admissible=mean(A, na.rm = T))

`summarise()` regrouping output by 'Estimator', 'ss_l2' (override with `.groups` argument)

c1 <- cbind(c[ c$Estimator == 'MLR', c('ss_l2', 'ss_l1', 'Admissible')], 
           c[ c$Estimator == 'ULSMV', 'Admissible'], 
           c[ c$Estimator == 'WLSMV', 'Admissible'])

New names:
* Admissible -> Admissible...3
* Admissible -> Admissible...4
* Admissible -> Admissible...5

colnames(c1) <- c('Level-2 N_2', "Level-1 N_1", 'MLR', 'ULSMV', 'WLSMV')


kable(c1, format='html', digits=3) %>%
  kable_styling(full_width = T)

Level-2 N_2	Level-1 N_1	MLR	ULSMV	WLSMV
30	5	0.575	0.401	0.355
30	10	0.685	0.573	0.508
30	30	0.838	0.762	0.688
50	5	0.677	0.532	0.470
50	10	0.796	0.707	0.660
50	30	0.902	0.880	0.820
100	5	0.806	0.692	0.665
100	10	0.920	0.873	0.824
100	30	0.946	0.945	0.911
200	5	0.923	0.875	0.849
200	10	0.963	0.958	0.936
200	30	0.975	0.974	0.973

print(xtable(c1, digits = 3,align=c("l", "l","l", "r", "r", "r"),
             display=c("s","s", "d", "f", "f", "f"),
             caption="Convergence rates indicate problems for all estimation methods",
             label="tb:admiss"),
      booktabs = T, include.rownames = F,
      caption.placement = "top")

Warning in formatC(x = c(30, 30, 30, 50, 50, 50, 100, 100, 100, 200, 200, :
coercing argument to "character" for format="s"

% latex table generated in R 4.0.0 by xtable 1.8-4 package
% Fri Jun 05 10:47:52 2020
\begin{table}[ht]
\centering
\caption{Convergence rates indicate problems for all estimation methods} 
\label{tb:admiss}
\begin{tabular}{llrrr}
  \toprule
Level-2 N\_2 & Level-1 N\_1 & MLR & ULSMV & WLSMV \\ 
  \midrule
 30 &    5 & 0.575 & 0.401 & 0.355 \\ 
   30 &   10 & 0.685 & 0.573 & 0.508 \\ 
   30 &   30 & 0.838 & 0.762 & 0.688 \\ 
   50 &    5 & 0.677 & 0.532 & 0.470 \\ 
   50 &   10 & 0.796 & 0.707 & 0.660 \\ 
   50 &   30 & 0.902 & 0.880 & 0.820 \\ 
  100 &    5 & 0.806 & 0.692 & 0.665 \\ 
  100 &   10 & 0.920 & 0.873 & 0.824 \\ 
  100 &   30 & 0.946 & 0.945 & 0.911 \\ 
  200 &    5 & 0.923 & 0.875 & 0.849 \\ 
  200 &   10 & 0.963 & 0.958 & 0.936 \\ 
  200 &   30 & 0.975 & 0.974 & 0.973 \\ 
   \bottomrule
\end{tabular}
\end{table}

sessionInfo()

R version 4.0.0 (2020-04-24)
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 datasets  utils     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_1.0.0          
[10] purrr_0.3.4           readr_1.3.1           tidyr_1.1.0          
[13] tibble_3.0.1          ggplot2_3.3.1         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        renv_0.10.0       cellranger_1.1.0  yaml_2.2.1       
[13] pillar_1.4.4      backports_1.1.7   lattice_0.20-41   glue_1.4.1       
[17] digest_0.6.25     promises_1.1.0    rvest_0.3.5       colorspace_1.4-1 
[21] htmltools_0.4.0   httpuv_1.5.3.1    plyr_1.8.6        pkgconfig_2.0.3  
[25] broom_0.5.6       haven_2.3.1       scales_1.1.1      webshot_0.5.2    
[29] whisker_0.4       later_1.0.0       git2r_0.27.1      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_3.1-147      xml2_1.3.2       
[45] tools_4.0.0       hms_0.5.3         lifecycle_0.2.0   munsell_0.5.0    
[49] reprex_0.3.0      compiler_4.0.0    rlang_0.4.6       grid_4.0.0       
[53] rstudioapi_0.11   texreg_1.37.1     rmarkdown_2.2     boot_1.3-24      
[57] gtable_0.3.0      DBI_1.1.0         R6_2.4.1          lubridate_1.7.8  
[61] knitr_1.28        rprojroot_1.3-2   stringi_1.4.6     parallel_4.0.0   
[65] Rcpp_1.0.4.6      vctrs_0.3.0       dbplyr_1.4.4      tidyselect_1.1.0 
[69] xfun_0.14         coda_0.19-3

ML-CFA: Convergence and Admissibility Summary

Convergence

Single Condition Breakdown

Estimation Method

Level-2 Sample Size

Level-1 Sample Size

ICC Observed Variables

ICC Latent Variables

Convergence by Estimation Method and Sample Sizes

Estimation Method & Level-2 Sample Size

Estimation Method & Level-1 Sample Size

Estimation Method, Level-2 Sample Size & Level-1 Sample Size

Across all conditions

Admissibility Summary

Single Condition Breakdown

Estimation Method

Level-2 Sample Size

Level-1 Sample Size

ICC Observed Variables

ICC Latent Variables

Admissible Replications by Estimation Method and Sample Sizes

Estimation Method & Level-2 Sample Size

Estimation Method & Level-1 Sample Size

Estimation Method, Level-2 Sample Size & Level-1 Sample Size

Admissible Replications for individual conditions

Manuscript Table(s)