DRPCA: Distributed Robust Principal Component Analysis (DRPCA) for...

In DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

Distributed Robust Principal Component Analysis (DRPCA) for Handling Missing Data

Description

This function performs DRPCA to handle missing data by dividing the dataset into D blocks, applying the Robust Principal Component Analysis (RPCA) method to each block, and then combining the results. It calculates various evaluation metrics including RMSE, MMAE, RRE, and Generalized Cross-Validation (GCV) using different hierarchical clustering methods.

Usage

DRPCA(data0, data.sample, data.copy, mr, km, D)

Arguments

data0	The original dataset containing the response variable and features.
data.sample	The dataset used for sampling, which may contain missing values.
data.copy	A copy of the original dataset, used for comparison or validation.
mr	Indices of the rows with missing values that need to be predicted.
km	The number of clusters for k-means clustering.
D	The number of blocks to divide the data into.

Value

A list containing:

XDRPCA	The imputed dataset.
RMSEDRPCA	The Root Mean Squared Error.
MAEDRPCA	The Mean Absolute Error.
REDRPCA	The Relative Eelative Error.
GCVDRPCA	Distributed DRPCA Imputation for Generalized Cross-Validation.
timeDRPCA	The DRPCA algorithm execution time.

See Also

RPCA for the original RPCA function.

Examples

# Create a sample dataset with missing values
set.seed(123)
n <- 100
p <- 10
D <- 2
data.sample <- matrix(rnorm(n * p), nrow = n)

# Randomly select 10 rows to have missing values
mr <- sort(sample(1:n, 10))
missing_cols <- sample(1:p, 2)
data.copy <- data.sample
for (i in mr) {
  data.sample[i, missing_cols] <- NA
}

data0 <- data.frame(data.sample, response = rnorm(n))
km <- 3
result <- DRPCA(data0, data.sample, data.copy, mr, km, D)
print(result$XDRPCA)

DTSR documentation built on June 20, 2026, 5:06 p.m.