Correlation among Parameter Estimates: Level-2 Factor Covariance
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
- Subsetting the data to the parameter of interest
- Reshaping the data to wide
- Compute the correlation
- 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
- Reshape to long data for plotting
- 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