MLPCA: Multilinear Principal Component Analysis with Missing Data

In DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

Multilinear Principal Component Analysis with Missing Data

Description

This function performs Multilinear Principal Component Analysis (MLPCA) to handle missing data by imputing the missing values based on the correlation structure within the data. It also calculates the RMSE and Consistency Proportion Index (CPP) using different hierarchical clustering methods.

Usage

MLPCA(data0, data.sample, data.copy, mr, km)

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.

Value

A list containing:

Xnew	The imputed dataset.
RMSE	The Root Mean Squared Error.
CPP1	The K-means clustering Consistency Proportion Index.
CPP2	The Hierarchical Clustering Complete Linkage Consistency Proportion Index.
CPP3	The Hierarchical Clustering Single Linkage Consistency Proportion Index.
CPP4	The Hierarchical Clustering Average Linkage Consistency Proportion Index.
CPP5	The Hierarchical Clustering Centroid linkage Consistency Proportion Index.
CPP6	The Hierarchical Clustering Median Linkage Consistency Proportion Index.
CPP7	The Hierarchical Clustering Ward's Method Consistency Proportion Index.
timeKNN	The MLPCA algorithm execution time.

See Also

princomp and svd for more information on PCA and SVD.

Examples

# Create a sample matrix with random values and introduce missing values
set.seed(123)
n <- 100
p <- 5
data.sample <- matrix(rnorm(n * p), nrow = n)
data.sample[sample(1:(n*p), 20)] <- NA
data.copy <- data.sample
data0 <- data.frame(data.sample, response = rnorm(n))
mr <- sample(1:n, 10)  # Sample rows for evaluation
km <- 3  # Number of clusters
# Perform MLPCA imputation
result <- MLPCA(data0, data.sample, data.copy, mr, km)
# Print the results
print(result$RMSE)
print(result$CPP1)
print(result$Xnew)

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