In MAnalytics/akmedoids: Anchored Kmedoids for Longitudinal Data Clustering

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akmedoids

An R package for analyzing and clustering longitudinal data

Description

The akmedoids package advances the clustering of longitudinal datasets in order to identify clusters of trajectories with similar long-term linear trends over time, providing an improved cluster identification as compared with the classic kmeans algorithm. The package also includes a set of functions for addressing common data issues, such as missing entries and outliers, prior to conducting advance longitudinal data analysis. One of the key objectives of this package is to facilitate easy replication of a recent paper which examined small area inequality in the crime drop (Adepeju et al. 2020). Many of the functions provided in the akmedoids package may be applied to longitudinal data in general.

For more information and usability, check out details on CRAN.

Installation from CRAN

From an R console, type:

#install.packages("akmedoids")
library(akmedoids)

#Other libraries
library(tidyr)
library(ggplot2)
library(reshape)
library(readr)

To install the development version of the package, type remotes::install_github("MAnalytics/akmedoids"). Please, report any installation problems in the issues.

Example usage:

Given a longitudinal datasets, the following is an example of how akmedoids could be used to extract clusters of trajectories with similar long-term trends over time. We will use a simulated dataset (named simulated.rda) stored in the data/ directory.

Generating artificial dataset

Simulating data set which comprised of three clusters with distinct mean directional change over time. Each group contains 50 trajectories.

dir.create("input") # create a folder

#function for creating longitudinal noise
noise_fn <- function(x=3, time){
  rnorm(length(time), mean=0, x)}

#function for simulating a trajectory group
sim_group <- function(gr_baseline, sd, time){
  intcp_errors <- rgamma(1, shape=2, scale=sd) #intercept error
  mean_traj = gr_baseline + intcp_errors
  traj = mean_traj + noise_fn(intcp_errors, time)
}

#time steps
t_steps <- c(0:20)

#increasing group
i_gr <- NULL
for(i in seq_len(50)){
  i_gr <- rbind(i_gr,
                sim_group(gr_baseline=(0.5*t_steps),
                          sd=1, time=t_steps))
}

#stable group
s_gr <- NULL
for(i in seq_len(50)){
  s_gr <- rbind(s_gr,
                sim_group(gr_baseline=rep(3,length(t_steps)),
                          sd=1, time=t_steps))
}

#decreasing group
d_gr <- NULL
for(i in seq_len(50)){
  d_gr <- rbind(d_gr,
                sim_group(gr_baseline=(10 - (0.5*t_steps)),
                          sd=1, time=t_steps))
}

#combine groups
simulated <- data.frame(rbind(i_gr, s_gr, d_gr))

#add group label
simulated <- data.frame(cbind(ID=1:nrow(simulated), simulated))

colnames(simulated) <- c("ID", 1:(ncol(simulated)-1))

#save data set
##simulated = readr::write_csv(simulated, "input/example-simulated.csv")

Visualising artificial dataset

#import already save simulated data
Import_simulated = read_csv(file="./input/example-simulated.csv")

#preview the data
head(Import_simulated)


#convert wide-format into long
simulated_long <- melt(t(Import_simulated), id.vars=c("ID"))

 simulated_long <- simulated_long %>%
  dplyr::filter(X1!="ID") %>%
  dplyr::rename(Time=X1, ID=X2)
# 
simulated_long <- data.frame(cbind(simulated_long,
                                   Groups= c(rep("Increasing", 50*21),
                                   rep("Stable", 50*21),
                                   rep("Decreasing", 50*21))))

#re-order levels
simulated_long$Time <- factor(simulated_long$Time, 
                                levels = c(1:21))

ggplot(simulated_long, aes(x = Time, y = value, group=ID, color=Groups)) +
  geom_point(size=0.5) + 
  geom_line() +
  scale_color_manual(values=c('#999999','#E69F00', '#56B4E9')) +
  theme_light()

Performing clustering using akmedoids

Performing clustering analysis using akmedoids package.

output <- akclustr(Import_simulated, id_field=TRUE, verbose = FALSE, k=c(3,12), crit = "Silhouette",
                  quality_plot=TRUE)

#Remark: The entire output can be printed out to the console by typing `output`

#To check the optimal cluster size, type:
output$optimal_k

#Show quality plot
#plot(output$QltyPlot)

#user may decide the examine the plot post clustering....

Documentation

From an R console type ??akmedoids for help on the package. The package page on CRAN is here, package reference manual is here, package vignette is here.

Support and Contributions:

For support and bug reports send an email to: [e-mail] or open an issue here. Code contributions to akmedoids are also very welcome.

References:

Rousseeuw, P. J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics, no. 20: 53–6. link

Caliński, T., and J. Harabasz. 1974. “A Dendrite Method for Cluster Analysis.” Communications in Statistics-Theory and Methods, 3(1): 1–27. link

Adepeju, M., Langton, S. and Bannister, J. 2020. Anchored k-medoids: a novel adaptation of k-means further refined to measure instability in the exposure to crime. Journal of Computational Social Science, (revised).

MAnalytics/akmedoids documentation built on Aug. 28, 2021, 10:19 p.m.