Column Mean Imputation

Impute missing values in a matrix by replacing them with the mean of their respective columns.

Usage

mean_imp_col(obj, subset = NULL, cores = 1)

Arguments

obj

A numeric matrix.

subset

Optional character or integer vector specifying columns to impute. If NULL, all columns are imputed.

cores

Integer. Number of cores to use for parallel computation. Defaults to 1.

Value

A numeric matrix of the same dimensions as obj, with missing values in the selected columns replaced by column means.

Details

Columns with no observed values cannot be imputed by their column mean and are left unchanged.

Examples

obj <- matrix(c(1, 2, NA, 4, NA, 6, NA, 8, 9, NA, NA, NA), nrow = 3)
colnames(obj) <- c("A", "B", "C", "D")
obj
#>       A  B  C  D
#> [1,]  1  4 NA NA
#> [2,]  2 NA  8 NA
#> [3,] NA  6  9 NA

# impute missing values with column means
mean_imp_col(obj)
#>        A B   C  D
#> [1,] 1.0 4 8.5 NA
#> [2,] 2.0 5 8.0 NA
#> [3,] 1.5 6 9.0 NA

# impute only specific columns by name
mean_imp_col(obj, subset = c("A", "C"))
#>        A  B   C  D
#> [1,] 1.0  4 8.5 NA
#> [2,] 2.0 NA 8.0 NA
#> [3,] 1.5  6 9.0 NA

# impute only specific columns by index
mean_imp_col(obj, subset = c(1, 3))
#>        A  B   C  D
#> [1,] 1.0  4 8.5 NA
#> [2,] 2.0 NA 8.0 NA
#> [3,] 1.5  6 9.0 NA