PAMhm: Generate Heatmaps Based on Partitioning Around Medoids (PAM)
Data are partitioned (clustered) into k clusters "around medoids", which is
a more robust version of K-means implemented in the function pam() in the 'cluster' package.
The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>.
Please refer to the pam() function documentation for more references.
Clustered data is plotted as a split heatmap allowing visualisation of representative
"group-clusters" (medoids) in the data as separated fractions of the graph while those
"sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.
| Version: |
0.1.2 |
| Depends: |
heatmapFlex, cluster, grDevices, graphics, stats |
| Imports: |
RColorBrewer, R.utils, readxl, readmoRe, utils, plyr, robustHD |
| Suggests: |
rmarkdown, knitr |
| Published: |
2021-09-06 |
| Author: |
Vidal Fey [aut, cre],
Henri Sara [aut] |
| Maintainer: |
Vidal Fey <vidal.fey at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
PAMhm results |
Documentation:
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