The “missForest” Function in R

• Package: missForest

• Purpose: Impute missing values in a dataset using random forests.

• General class: Imputation

• Required argument(s):

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

• Notable optional arguments:

• maxiter: Maximum number of iterations to be performed (default is 10).

• ntree: Number of trees to grow in each forest (default is 100).

• mtry: Number of variables randomly sampled as candidates at each split (default is the square root of the number of variables).

• replace: Logical. Whether sampling of observations is with or without replacement (default is TRUE).

• verbose: Logical. Whether to print progress of the imputation (default is FALSE).

• variablewise: Logical. Whether to perform variablewise or casewise imputation (default is FALSE).

• Example:

• # Load the required library
  library(missForest)
  
  # Create a sample dataset with missing values
  set.seed(123)
  data <- iris
  data[sample(1:nrow(data), 20), sample(1:ncol(data), 2)] <- NA
  
  # Perform imputation using the missForest function
  imputed_data <- missForest(data, maxiter = 5, ntree = 100, verbose = TRUE)
  
  # View the imputed dataset
  print(imputed_data$ximp)

• In this example, the missForest function from the missForest package is used to impute missing values in a sample dataset based on the Iris dataset. The imputed dataset is stored in the imputed_data$ximp object, which can then be used for further analysis. The maxiter argument limits the number of iterations to 5, and the ntree argument sets the number of trees to 100. The verbose argument is set to TRUE to display the progress of the imputation.