DTSR: Distributed Trimmed Scores Regression (DTSR) for Handling...

In DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

Distributed Trimmed Scores Regression (DTSR) for Handling Missing Data

Description

This function performs DTSR to handle missing data by dividing the dataset into D blocks, applying the Trimmed Scores Regression (TSR) method to each block, and then combining the results. It calculates various evaluation metrics including RMSE, MMAE, RRE, and Consistency Proportion Index (CPP) using different hierarchical clustering methods.

Usage

DTSR(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:

XDTSR	The imputed dataset.
RMSEDTSR	The Root Mean Squared Error.
MAEDTSR	The Mean Absolute Error.
REDTSR	The Relative Eelative Error.
GCVDTSR	The DTSR for Generalized Cross-Validation.
timeDTSR	The DTSR algorithm execution time.

See Also

TSR for the original TSR function.

Examples

# Create a sample matrix with random values and introduce missing values
set.seed(123)
n <- 100
p <- 10
D <- 2
data.sample <- matrix(rnorm(n * p), nrow = n)
data.sample[sample(1:(n-10), (p-2))] <- 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 DTSR imputation
result <- DTSR(data0, data.sample, data.copy, mr, km,D)
# Print the results
print(result$XDTSR)

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