The “mice” Function in R

• Package: mice

• Purpose: Perform multiple imputation to handle missing data.

• General class: Imputation

• Required argument(s):

• data: A data frame or matrix with missing values.

• Notable optional arguments:

• m: Number of multiple imputations to generate (default is 5).

• method: Method of imputation (default is pmm for predictive mean matching).

• maxit: Maximum number of iterations (default is 5).

• seed: Seed for random number generation to ensure reproducibility.

• printFlag: Logical, indicating whether to print progress messages (default is TRUE).

• Example:

• # Load the required library
  library(mice)
  
  # Create a sample data frame with missing values
  data <- data.frame(
  age = c(25, 30, NA, 40, 35, NA),
  income = c(50000, 55000, 60000, NA, 65000, 70000)
  )
  
  # Perform multiple imputation
  imputed_data <- mice(data, m = 5, method = 'pmm', maxit = 5, seed = 123)
  
  # Print the imputation result
  print(imputed_data)
  
  # Extract the first completed data set
  completed_data <- complete(imputed_data)
  
  # Print the completed data
  print(completed_data)

• This example demonstrates how to handle missing data using the mice function from the mice package. The data argument takes a data frame or matrix with missing values. Optional arguments like m, method, maxit, seed, and printFlag can be used to customize the multiple imputation process.