R functions (M)

The list of new R functions introduced in this Missing data analysis lab component are below:

Function_name	Package_name	Use
aggr	VIM	To calculate/plot the missing values in the variables
boxplot	base/graphics	To produce a box plot
bwplot	mice	To produce box plot to compare the imputed and observed values
colMeans	base	To compute the column-wise mean, i.e., mean for each variable/column
complete	mice	To extract the imputed dataset
complete.cases	base/stats	To select the complete cases, i.e., observations without missing values
D1	mice	To conduct the multivariate Wald test with D1-statistic
densityplot	mice	To produce desnsity plots
expression	base	To set/create an expression
imputationList	mice	To combine multiple imputed datasets
marginplot	VIM	To draw a scatterplot with additional information when there are missing values
mcar_test	naniar	To conduct Little's MCAR test
md.pattern	mice	To see the pattern of the missing data
mice	mice	To impute missing data where the argument m represents the number of multiple imputation
MIcombine	mitools	To combine/pool the results using Rubin's rule
MIextract	mitools	To extract parameters from a list of outputs
na.test	misty	To conduct Little's MCAR test
parlmice	mice	To run `mice` function in parallel, i.e., parallel computing of mice
plot_missing	DataExplorer	To plot the profile of missing values, e.g., the percentage of missing per variable
pool	mice	To pool the results using Rubin's rule
pool.compare	mice	To compare two nested models
pool_mi	miceadds	To combine/pool the results using Rubin's rule
quickpred	mice	To set imputation model based on the correlation
sim_slopes	interactions	To perform simple slope analyses
TestMCARNormality	MissMech	To test multivariate normality and homoscedasticity in the context of missing data
unlist	base	To convert a list to a vector