KNN: This function performs imputation using the K-Nearest...

In DTSR: Distributed Trimmed Scores Regression for Handling Missing Data

This function performs imputation using the K-Nearest Neighbors (KNN) algorithm and calculates various evaluation metrics including RMSE, MMAE, RRE, and Consistency Proportion Index (CPP) using different hierarchical clustering methods. It also records the execution time of the process.

Description

This function performs imputation using the K-Nearest Neighbors (KNN) algorithm and calculates various evaluation metrics including RMSE, MMAE, RRE, and Consistency Proportion Index (CPP) using different hierarchical clustering methods. It also records the execution time of the process.

Usage

KNN(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.
timeKNN	The KNN algorithm execution time.

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