mean: Mean Imputation with Evaluation Metrics

In DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

Mean Imputation with Evaluation Metrics

Description

This function performs mean imputation on a dataset with missing values. It replaces missing values with the column means and calculates various evaluation metrics including RMSE, MMAE, and RRE. Additionally, it performs k-means and hierarchical clustering to assess the quality of the imputation.

Usage

mean(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.
MMAE	The Mean Absolute Error.
RRE	The Relative Eelative 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.
timemean	The mean algorithm execution time.

See Also

kmeans in the stats package for more information on k-means clustering.

hclust in the stats package for more information on hierarchical clustering.

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 mean imputation
result <- mean(data0, data.sample, data.copy, mr, km)
# Print the results
print(result$RMSE)
print(result$MMAE)
print(result$RRE)
print(result$CPP1)
print(result$Xnew)

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