SVDImpute: Improved SVD Imputation

In DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

Improved SVD Imputation

Description

This function performs imputation using Singular Value Decomposition (SVD) with iterative refinement. It begins by filling missing values with the mean of their respective columns. Then, it computes a low-rank (k) approximation of the data matrix. Using this approximation, it refills the missing values. This process of recomputing the rank-k approximation with the newly imputed values and refilling the missing data is repeated for a specified number of iterations, 'num.iters'.

Usage

SVDImpute(x, k, num.iters = 10, verbose = TRUE)

Arguments

x	A data frame or matrix where each row represents a different record.
k	The rank-k approximation to use for the data matrix.
num.iters	The number of times to compute the rank-k approximation and impute the missing data.
verbose	If TRUE, print status updates during the process.

Value

A list containing:

data.matrix

The imputed matrix with missing values filled.

Examples

# Create a sample matrix with random values and introduce missing values
x = matrix(rnorm(100), 10, 10)
x[x > 1] = NA

# Perform SVD imputation
imputed_x = SVDImpute(x, 3)

# Print the imputed matrix
print(imputed_x)

DTSR documentation built on April 3, 2025, 11:35 p.m.