| Type: | Package |
| Date: | 2025-02-14 |
| Title: | Robust Panel Clustering Algorithm |
| Version: | 1.4 |
| Author: | Hasan Bulut [aut, cre] |
| Maintainer: | Hasan Bulut <hasan.bulut@omu.edu.tr> |
| Description: | Performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) <doi:10.25236/AJBM.2021.031018>, Cuesta-Albertos et al. (1997) https://www.jstor.org/stable/2242558?seq=1. |
| License: | GPL-2 |
| Depends: | R (≥ 4.0) |
| Imports: | stats, trimcluster |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-02-18 19:16:43 UTC; hsn |
| Repository: | CRAN |
| Date/Publication: | 2025-02-20 17:30:06 UTC |
Robust Panel Clustering Algorithm
Description
Robust Panel Clustering Algorithm
Usage
RobPC(data, k, robclust = FALSE, trim = 0.25)
Arguments
data |
the panel data. It must be array. |
k |
the number of clusters. |
robclust |
a logical arguments. If robclust=TRUE, the function implements the robust panel clustering analysis. Otherwise, it implements the traditional panel clustering analysis. The default value is robclust=TRUE. |
trim |
numeric between 0 and 1. Proportion of points to be trimmed. The default value is 0.25. |
Details
RobPC function implements the traditional or robust panel clustering analysis without being affected by outliers in the panel data.
Value
a list with 2 elements:
clusters |
integer vector coding cluster membership. If robclust=TRUE, this vector includes codes as k+1 to define outliers clusters. |
clustering_method |
The used clustering method which is "Robust Panel Clustering" or "Panel Clustering". |
Author(s)
Hasan BULUT <hasan.bulut@omu.edu.tr>
References
Bulut et al. (Unpublished). A Robust Clustering Algorithms for Panel Data.
Wang, W., & Lu, Y. (2021). Application of clustering analysis of panel data in economic and social research based on R software. Acad. J. Bus. Manag, 3, 98-104.
Cuesta-Albertos, J. A., Gordaliza, A., & Matrán, C. (1997). Trimmed k-means: an attempt to robustify quantizers. The Annals of Statistics, 25(2), 553-576.
Examples
set.seed(123)
n_obs <- 10 # Number of observations (N)
n_time <- 5 # Number of time periods (T)
n_vars <- 3 # Number of variables (D)
data <- array(rnorm(n_obs * n_time * n_vars), dim = c(n_obs, n_time, n_vars))
# Apply the Classical Panel Clustering
result_classic <- RobPC(data,k=3,robclust = FALSE)
result_classic
# Apply the Robust Panel Clustering
result_robust<- RobPC(data,k=3,robclust = TRUE,trim=0.2)
result_robust