First, we load the ‘ILSE’ package and the real data which can be loaded by following command.
We fit the linear regression model using ‘ILSE’ function, and then compare with CC method and FIML method.
ncomp <- sum(complete.cases(nhanes))
message("Number of complete cases is ", ncomp, '\n')
ilse2 <- ilse(age~., data=nhanes, verbose=T)
print(ilse2)Next, Bootstrap is applied to evaluate the standard error and p-values of each coefficients estimated by ILSE. We observe four significant coefficients.
First, we conduct CC analysis.
We fit linear regression model using FIML method.
We also use bootstrap to evaluate the standard error and p-values of each coefficients estimated by ILSE. We observe only one significant coefficients.
We visualize the p-vaules of each methods, where red line denotes 0.05 in y-axis and blue line 0.1 in y-axis.
library(ggplot2)
library(ggthemes)
pMat <- cbind(CC=s_cc$coefficients[,4], ILSE=s2[,4], FIML=s_fiml[,4])
df1 <- data.frame(Pval= as.vector(pMat[-1,]),
Method =factor(rep(c('CC', "ILSE", "FIML"),each=3)),
covariate= factor(rep(row.names(pMat[-1,]), times=3)))
ggplot(data=df1, aes(x=covariate, y=Pval, fill=Method)) + geom_bar(position = "dodge", stat="identity",width = 0.5) + geom_hline(yintercept = 0.05, color='red') + geom_hline(yintercept = 0.1, color='blue') +
scale_fill_economist()sessionInfo()
#> R version 4.0.3 (2020-10-10)
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#> Running under: Windows 10 x64 (build 22000)
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#> [2] LC_CTYPE=Chinese (Simplified)_China.936
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#> [4] LC_NUMERIC=C
#> [5] LC_TIME=Chinese (Simplified)_China.936
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> [1] digest_0.6.28 R6_2.5.1 jsonlite_1.7.2 magrittr_2.0.1
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#> [9] bslib_0.3.1 rmarkdown_2.11 tools_4.0.3 stringr_1.4.0
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