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.
| Version: |
0.1.5 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
kml, Hmisc, ggplot2, utils, reshape2, longitudinalData, stats, signal |
| Suggests: |
knitr, rmarkdown, flextable, kableExtra, clusterCrit |
| Published: |
2020-01-10 |
| Author: |
Monsuru Adepeju [cre, aut], Samuel Langton [aut], Jon Bannister [aut] |
| Maintainer: |
Monsuru Adepeju <monsuur2010 at yahoo.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
akmedoids results |