sample_na_loc: Sample Missing-Value Locations with Constraints

In slideimp: Numeric Matrices K-NN and PCA Imputation

Sample Missing-Value Locations with Constraints

Description

Sample matrix indices for NA injection while respecting row and column missingness limits and avoiding zero-variance columns.

Usage

sample_na_loc(
  obj,
  n_cols = NULL,
  n_rows = 2L,
  num_na = NULL,
  n_reps = 1L,
  rowmax = 0.9,
  colmax = 0.9,
  na_col_subset = NULL,
  max_attempts = 100
)

Arguments

obj	A numeric matrix.
n_cols	Integer or NULL. Number of columns to receive injected missing values. Must be supplied when num_na = NULL.
n_rows	Integer. Target number of missing values to inject per selected column.
num_na	Integer or NULL. Total number of missing values to inject per repetition. If supplied, n_cols is derived from num_na and n_rows, and missing values are distributed as evenly as possible across columns.
n_reps	Integer. Number of independent repetitions.
rowmax	Numeric scalar between 0 and 1. Maximum allowed missing-data proportion per row after injection.
colmax	Numeric scalar between 0 and 1. Maximum allowed missing-data proportion per column after injection.
na_col_subset	Optional integer or character vector restricting which columns are eligible for missing-value injection.
max_attempts	Integer. Maximum number of resampling attempts per repetition before giving up.

Details

The function uses a greedy stochastic search for valid NA locations. It ensures that:

• Total missingness per row and column does not exceed rowmax and colmax.

• At least two distinct observed values are preserved in every affected column.

Value

A list of length n_reps. Each element is a two-column integer matrix with row and column indices for sampled NA locations.

Examples

set.seed(123)
mat <- matrix(runif(100), nrow = 10)

# Sample 5 missing values across 5 columns
locs <- sample_na_loc(mat, n_cols = 5, n_rows = 1)
locs

# Inject the missing values from the first repetition
mat[locs[[1]]] <- NA
mat

slideimp documentation built on June 17, 2026, 1:08 a.m.