traj

Simulated longitudinal datasets containing
trajectories with missing values
(<code>NA</code>, <code>Inf</code>, <code>null</code>)

datasets

Advances a novel adaptation of longitudinal k-means clustering
technique (Genolini et al. (2015) <doi:10.18637/jss.v065.i04>)
for grouping trajectories based on the similarities of their
long-term trends and determines the optimal solution based
on either the average silhouette width (Rousseeuw P. J. 1987)
or the Calinski-Harabatz criterion (Calinski and Harabatz (1974)
<doi:10.1080/03610927408827101>). Includes functions to extract
descriptive statistics and generate a visualisation of the
resulting groups, drawing methods from the 'ggplot2' library (Wickham H. (2016)
<doi:10.1007/978-3-319-24277-4>). The package also includes a number of
other useful functions for exploring and manipulating longitudinal
data prior to the clustering process.

Monsuru Adepeju

akmedoids

Anchored Kmedoids for Longitudinal Data Clustering

traj function

A dataframe with the following variables:<ul>
<li>location_ids: Character id of sample locations
 at which values are obtained.</li>
<li>X2001: Values at time step 1 (i.e. year 2001)</li>
<li>X2002: Values at time step 2 (i.e. year 2002)</li>
<li>X2003: Values at time step 3 (i.e. year 2003)</li>
<li>X2004: Values at time step 4 (i.e. year 2004)</li>
<li>X2005: Values at time step 5 (i.e. year 2005)</li>
<li>X2006: Values at time step 6 (i.e. year 2006)</li>
<li>X2007: Values at time step 7 (i.e. year 2007)</li>
<li>X2008: Values at time step 8 (i.e. year 2008)</li>
<li>X2009: Values at time step 9 (i.e. year 2009)</li>
</ul>

traj: Sample longitudinal dataset

Description

Usage

Arguments

Format