missingness_indicators: Return matrix of missingness indicators for a dataframe or...

In ck37/ck37r: Chris Kennedy's R Toolkit

Return matrix of missingness indicators for a dataframe or matrix.

Description

Return matrix of missingness indicators for a dataframe or matrix. Removes constant or collinear indicators.

Usage

missingness_indicators(
  data,
  prefix = "miss_",
  remove_constant = TRUE,
  remove_collinear = TRUE,
  skip_vars = c(),
  verbose = FALSE
)

Arguments

data	Dataframe or matrix to analyze for missingness.
prefix	Name prefix for new indicator columns.
remove_constant	Remove any indicators that are all 0 or all 1.
remove_collinear	Remove any indicators that are collinear with each other.
skip_vars	Vector of variable names to skip.
verbose	If TRUE, print additional information.

Value

Matrix of missingness indicators

See Also

impute_missing_values

Examples

# Load a test dataset.
data(PimaIndiansDiabetes2, package = "mlbench")

# Check for missing values.
colSums(is.na(PimaIndiansDiabetes2))

# Generate missingness indicators; skip outcome variable.
indicators = missingness_indicators(PimaIndiansDiabetes2,
                                    skip_vars = "diabetes")

# Check missingness.
colSums(indicators)

ck37/ck37r documentation built on April 29, 2023, 11:42 p.m.