Imputation vs. Listwise Deletion (Example in R) | Handle Missing Values | Substitute & Complete Data скачать в хорошем качестве

Imputation vs. Listwise Deletion (Example in R) | Handle Missing Values | Substitute & Complete Data

This video shows a comparison of Missing Data Imputation & Listwise Deletion in the R programming language. Check out my online course on Missing Data Imputation in R: https://statisticsglobe.com/online-wo... R code of this video: install.packages("ggplot2") # Install & load ggplot2 library(ggplot2) install.packages("mice") # Install & load mice library(mice) set.seed(875526) # Seed for reproducibility n_sample ＜- 100000 # Specify sample size x1 ＜- round(rnorm(n_sample), 2) # Create x variables x2 ＜- round(rnorm(n_sample) + 0.1 * x1, 2) x3 ＜- round(rnorm(n_sample) - 0.4 * x1 - 0.1 * x2, 2) x4 ＜- round(rnorm(n_sample) - 0.2 * x1 - 0.3 * x2 + 0.1 * x3, 2) y ＜- round(rnorm(n_sample) + 0.2 * x1 + 0.1 * x2 - 0.3 * x3 + 0.1 * x4, 2) # y df_true ＜- data.frame(x1, x2, x3, x4, y) # Create true data frame head(df_true) # Head of true data df_mcar ＜- df_true # Insert MCAR missingness df_mcar$y[sample(1:n_sample, 0.25 * n_sample)] ＜- NA head(df_mcar) # Head of MCAR data df_mar ＜- df_true # Insert MAR missingness rm_miss_mar ＜- - 0.8 * x1 - 0.7 * x2 + 0.9 * x3 - 0.8 * x4 + rnorm(n_sample) dummy_miss_mar ＜- rep(1, n_sample) dummy_miss_mar[rm_miss_mar ＜ quantile(rm_miss_mar)[2]] ＜- 0 df_mar$y[dummy_miss_mar == 0] ＜- NA head(df_mar) # Head of MAR data df_mcar_list ＜- na.omit(df_mcar) # Listwise deletion df_mar_list ＜- na.omit(df_mar) dim(df_mcar_list) # Rows after deletion dim(df_mar_list) round(mean(df_true$y), 3) # Means after deletion round(mean(df_mcar_list$y), 3) round(mean(df_mar_list$y), 3) ggplot() + # Densities after deletion geom_density(data = df_true, aes(x = y, color = "True Values")) + geom_density(data = df_mcar_list, aes(x = y, color = "MCAR")) + geom_density(data = df_mar_list, aes(x = y, color = "MAR")) + guides(color = guide_legend(title = NULL)) + ggtitle("Densities after Listwise Deletion") df_mcar_mice ＜- complete(mice(df_mcar, m = 1)) # Imputation df_mar_mice ＜- complete(mice(df_mar, m = 1)) dim(df_mcar_mice) # Rows after deletion dim(df_mar_mice) round(mean(df_true$y), 3) # Means after imputation round(mean(df_mcar_mice$y), 3) round(mean(df_mar_mice$y), 3) ggplot() + # Densities after deletion geom_density(data = df_true, aes(x = y, color = "True Values")) + geom_density(data = df_mcar_mice, aes(x = y, color = "MCAR")) + geom_density(data = df_mar_mice, aes(x = y, color = "MAR")) + guides(color = guide_legend(title = NULL)) + ggtitle("Densities after mice Imputation") Follow me on Social Media: LinkedIn – Joachim Schork Profile:   / linkedin   – Statistics Globe Page:   / statisticsglobe   LinkedIn – R Programming Group for Discussions & Questions:   / 12555223   LinkedIn – Python Programming Group for Discussions & Questions:   / 12673534   X (Formerly Twitter): https://x.com/JoachimSchork Facebook – Joachim Schork Profile:   / facebook   – Statistics Globe Page:   / statisticsglobecom   Facebook – R Programming Group for Discussions & Questions:   / statisticsglobe   Facebook – Python Programming Group for Discussions & Questions:   / statisticsglobepython   Instagram:   / statisticsglobecom   TikTok:   / statisticsglobe