Last updated: 2020-05-06

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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"))

# subset to data with admissible replications
sim_results <- filter(sim_results, Converge==1 & Admissible==1)
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      MplusAutomation_0.7-3
 [4] data.table_1.12.8     patchwork_1.0.0       forcats_0.5.0        
 [7] stringr_1.4.0         dplyr_0.8.5           purrr_0.3.4          
[10] readr_1.3.1           tidyr_1.0.2           tibble_3.0.1         
[13] ggplot2_3.3.0         tidyverse_1.3.0       workflowr_1.6.1      

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_1.1.6   reprex_0.3.0      evaluate_0.14    
[13] coda_0.19-3       httr_1.4.1        pillar_1.4.3      rlang_0.4.5      
[17] readxl_1.3.1      rstudioapi_0.11   texreg_1.36.23    gsubfn_0.7       
[21] rmarkdown_2.1     proto_1.0.0       webshot_0.5.2     pander_0.6.3     
[25] munsell_0.5.0     broom_0.5.6       compiler_3.6.3    httpuv_1.5.2     
[29] modelr_0.1.7      xfun_0.13         pkgconfig_2.0.3   htmltools_0.4.0  
[33] tidyselect_1.0.0  viridisLite_0.3.0 fansi_0.4.1       crayon_1.3.4     
[37] dbplyr_1.4.3      withr_2.2.0       later_1.0.0       grid_3.6.3       
[41] nlme_3.1-144      jsonlite_1.6.1    gtable_0.3.0      lifecycle_0.2.0  
[45] DBI_1.1.0         git2r_0.26.1      magrittr_1.5      scales_1.1.0     
[49] cli_2.0.2         stringi_1.4.6     fs_1.4.1          promises_1.1.0   
[53] xml2_1.3.2        ellipsis_0.3.0    generics_0.0.2    vctrs_0.2.4      
[57] boot_1.3-24       tools_3.6.3       glue_1.4.0        hms_0.5.3        
[61] parallel_3.6.3    yaml_2.2.1        colorspace_1.4-1  rvest_0.3.5      
[65] knitr_1.28        haven_2.2.0      

General Descrition

On this page, we are investigating the correlation among parameter estimates between estimation methods. We do this by

  1. Subsetting the data to the parameter of interest
  2. Reshaping the data to wide
  3. Compute the correlation
  4. Compute the proportion of admissible cases by comparison (i.e., proportion of replications that were used to compute correlation). This gives us an indication of how convergence varied across estimation methods
  5. Reshape to long data for plotting
  6. Plot correlation estimates in boxplots with points overlayed

Level-2 Factor Covaraince

Data Manipulation

# keep variables
keepVar <- c("Condition", "Replication", "ss_l1", "ss_l2", "icc_ov", "icc_lv", "Estimator", "psiB12")

sim_res1 <- sim_results[, keepVar]

sim_res1 <- sim_res1%>%
  pivot_wider(id_cols=c("Condition","Replication", "ss_l1", "ss_l2", "icc_ov", "icc_lv"),
              names_from = "Estimator",
              values_from = "psiB12")

cor.est <- sim_res1 %>%
  group_by(ss_l1, ss_l2, icc_ov, icc_lv) %>%
  summarise(
    r_mlr_ulsmv = cor(MLR, ULSMV,use = "pairwise.complete"),
    cprop_mlr_ulsmv = (1-(sum(is.na(MLR + ULSMV))/500)) ,
    r_mlr_wlsmv = cor(MLR, WLSMV,use = "pairwise.complete"),
    cprop_mlr_wlsmv = (1-(sum(is.na(MLR + WLSMV))/500)),
    r_ulsmv_wlsmv = cor(ULSMV, WLSMV,use = "pairwise.complete"),
    cprop_ulsmv_wlsmv = (1-(sum(is.na(ULSMV + WLSMV))/500))
  )

a1 <- cor.est %>%
  pivot_longer(cols= starts_with("r_"),
               names_to= "Cor",
               values_to = "Est") %>%
  mutate(Cor = substring(Cor, 3))

a2 <- cor.est %>%
  pivot_longer(cols= starts_with("cprop_"),
               names_to= "Cor",
               values_to = "Cprop")%>%
  mutate(Cor = substring(Cor, 7))

cor.est <- left_join(a1[,c(1:4,8:9)], a2[,c(1:4,8:9)])
Joining, by = c("ss_l1", "ss_l2", "icc_ov", "icc_lv", "Cor")
cor.est$Cor <- factor(cor.est$Cor,
                      levels=c("mlr_ulsmv", "mlr_wlsmv", "ulsmv_wlsmv"),
                      labels=c("cor(MLR, ULSMV)",
                               "cor(MLR, WLSMV)",
                               "cor(ULSMV, WLSMV)"),
                      ordered=T)

cor.est$C90 <- as.factor(ifelse(cor.est$Cprop >= 0.9, ">= 90%", "< 90%"))
cor.est$C95 <- as.factor(ifelse(cor.est$Cprop >= 0.95, ">= 95%", "< 95%"))

Plots Between Conditions

cols=c("< 90%"="#56B4E9", ">= 90%"="#CC79A7")
p <- ggplot(cor.est, aes(x=Cor, y=Est)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(data=filter(cor.est, C90=="< 90%"),
              width=0.3, aes(x=Cor, y=Est, color="< 90%")) +
  geom_jitter(data=filter(cor.est, C90==">= 90%"),
              width=0.3, aes(x=Cor, y=Est, color=">= 90%")) +
  lims(y=c(0,1))+
  labs(y="Correlation between Estimates",
       x=NULL,
       title="Correlation amoung Estimates: Level-2 Factor Covariance")+
  scale_color_manual(name="% Admissible", values=cols)+
  theme_bw()+
  theme(panel.grid = element_blank())
p

p <- ggplot(cor.est, aes(x=Cor, y=Est)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(data=filter(cor.est, C90=="< 90%"),
              width=0.3, aes(x=Cor, y=Est, color="< 90%")) +
  geom_jitter(data=filter(cor.est, C90==">= 90%"),
              width=0.3, aes(x=Cor, y=Est, color=">= 90%")) +
  lims(y=c(0,1))+
  labs(y="Correlation between Estimates",
       x=NULL,
       title="Correlation amoung Estimates: Level-2 Factor Covariance",
       subtitle = "Conditional on Level-1 Sample Size")+
  scale_color_manual(name="% Admissible", values=cols)+
  facet_wrap(.~ss_l1)+
  theme_bw()+
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle=90))
p

p <- ggplot(cor.est, aes(x=Cor, y=Est)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(data=filter(cor.est, C90=="< 90%"),
              width=0.3, aes(x=Cor, y=Est, color="< 90%")) +
  geom_jitter(data=filter(cor.est, C90==">= 90%"),
              width=0.3, aes(x=Cor, y=Est, color=">= 90%")) +
  lims(y=c(0,1))+
  labs(y="Correlation between Estimates",
       x=NULL,
       title="Correlation amoung Estimates: Level-2 Factor Covariance",
       subtitle = "Conditional on Level-2 Sample Size")+
  scale_color_manual(name="% Admissible", values=cols)+
  facet_wrap(.~ss_l2, nrow=1)+
  theme_bw()+
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle=90))
p

p <- ggplot(cor.est, aes(x=Cor, y=Est)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(data=filter(cor.est, C90=="< 90%"),
              width=0.3, aes(x=Cor, y=Est, color="< 90%")) +
  geom_jitter(data=filter(cor.est, C90==">= 90%"),
              width=0.3, aes(x=Cor, y=Est, color=">= 90%")) +
  lims(y=c(0,1))+
  labs(y="Correlation between Estimates",
       x=NULL,
       title="Correlation amoung Estimates: Level-2 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

p <- ggplot(cor.est, aes(x=Cor, y=Est)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(data=filter(cor.est, C90=="< 90%"),
              width=0.3, aes(x=Cor, y=Est, color="< 90%")) +
  geom_jitter(data=filter(cor.est, C90==">= 90%"),
              width=0.3, aes(x=Cor, y=Est, color=">= 90%")) +
  lims(y=c(0,1))+
  labs(y="Correlation between Estimates",
       x=NULL,
       title="Correlation amoung Estimates: Level-2 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


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      MplusAutomation_0.7-3
 [4] data.table_1.12.8     patchwork_1.0.0       forcats_0.5.0        
 [7] stringr_1.4.0         dplyr_0.8.5           purrr_0.3.4          
[10] readr_1.3.1           tidyr_1.0.2           tibble_3.0.1         
[13] ggplot2_3.3.0         tidyverse_1.3.0       workflowr_1.6.1      

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_1.1.6   reprex_0.3.0      evaluate_0.14    
[13] coda_0.19-3       httr_1.4.1        pillar_1.4.3      rlang_0.4.5      
[17] readxl_1.3.1      rstudioapi_0.11   texreg_1.36.23    gsubfn_0.7       
[21] rmarkdown_2.1     labeling_0.3      proto_1.0.0       webshot_0.5.2    
[25] pander_0.6.3      munsell_0.5.0     broom_0.5.6       compiler_3.6.3   
[29] httpuv_1.5.2      modelr_0.1.7      xfun_0.13         pkgconfig_2.0.3  
[33] htmltools_0.4.0   tidyselect_1.0.0  viridisLite_0.3.0 fansi_0.4.1      
[37] crayon_1.3.4      dbplyr_1.4.3      withr_2.2.0       later_1.0.0      
[41] grid_3.6.3        nlme_3.1-144      jsonlite_1.6.1    gtable_0.3.0     
[45] lifecycle_0.2.0   DBI_1.1.0         git2r_0.26.1      magrittr_1.5     
[49] scales_1.1.0      cli_2.0.2         stringi_1.4.6     farver_2.0.3     
[53] fs_1.4.1          promises_1.1.0    xml2_1.3.2        ellipsis_0.3.0   
[57] generics_0.0.2    vctrs_0.2.4       boot_1.3-24       tools_3.6.3      
[61] glue_1.4.0        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_2.2.0