PCAimpute: Principal Component Analysis based imputation

In MatrixCorrelation: Matrix Correlation Coefficients

Principal Component Analysis based imputation

Description

Imputation of missing data, NA, using Principal Component Analysis with iterative refitting and mean value updates. The chosen number of components and convergence parameters (iterations and tolerance) influence the precision of the imputation.

Usage

PCAimpute(X, ncomp, center = TRUE, max_iter = 20, tol = 10^-5)

Arguments

X	matrix object to perform PCA on.
ncomp	integer number of components.
center	logical indicating if centering (default) should be performed.
max_iter	integer number of iterations of PCA if sum of squared change in imputed values is above tol.
tol	numeric tolerance for sum of squared cange in imputed values.

Value

Final singular value decomposition, imputed X matrix and convergence metrics (sequence of sum of squared change and number of iterations).

Examples

X <- matrix(rnorm(12),3,4)
X[c(2,6,10)] <- NA
PCAimpute(X, 3)

MatrixCorrelation documentation built on April 19, 2022, 1:06 a.m.