Nothing
## ---- setup, echo=FALSE-------------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, collapse = TRUE, dpi=300)
## ---- eval = FALSE------------------------------------------------------------
# library(EvoPhylo)
## ---- include=FALSE-----------------------------------------------------------
devtools::load_all(".")
## ---- eval = FALSE------------------------------------------------------------
# #Load a character data matrix from your local directory to produce a Gower distance matrix
# dist_matrix <- get_gower_dist("DataMatrix.nex", numeric = FALSE)
# ## OR
# #Load an example data matrix 'DataMatrix.nex' that accompanies `EvoPhylo`.
# DataMatrix <- system.file("extdata", "DataMatrix.nex", package = "EvoPhylo")
# dist_matrix <- get_gower_dist(DataMatrix, numeric = FALSE)
## -----------------------------------------------------------------------------
data(characters)
dist_matrix <- get_gower_dist(characters, numeric = FALSE)
## ---- fig.width=6, fig.height=4, fig.align = "center", out.width = "70%"------
## Estimate and plot number of cluster against silhouette width
sw <- get_sil_widths(dist_matrix, max.k = 10)
plot(sw, color = "blue", size = 1)
## ---- fig.width=8, fig.height=5, fig.align = "center", out.width = "70%"------
## Generate and vizualize clusters with PAM under chosen k value.
clusters <- make_clusters(dist_matrix, k = 3)
plot(clusters)
## ---- eval = FALSE------------------------------------------------------------
# ## Write clusters to Nexus file for Mr. Bayes
# cluster_to_nexus(clusters, file = "Clusters_MB.txt")
#
# ## Write partitioned alignments to separate Nexus files for BEAUTi
# # Make reference to your original character data matrix in your local directory
# write_partitioned_alignments("DataMatrix.nex", clusters, file = "Clusters_BEAUTi.nex")
## ---- fig.width=10, fig.height=7, fig.align = "center", out.width = "100%"----
#User may also generate clusters with PAM and produce a graphic clustering (tSNEs)
clusters <- make_clusters(dist_matrix, k = 3, tsne = TRUE, tsne_dim = 3)
plot(clusters, nrow = 2, max.overlaps = 5)
## ---- eval = FALSE------------------------------------------------------------
# ## Write clusters to Nexus file for Mr. Bayes
# cluster_to_nexus(clusters, file = "Clusters_MB.txt")
#
# ## Write partitioned alignments to separate Nexus files for BEAUTi
# # Make reference to your original character data matrix in your local directory
# write_partitioned_alignments("DataMatrix.nex", clusters, file = "Clusters_BEAUTi.nex")
## ---- eval = FALSE------------------------------------------------------------
# #Load a character data matrix from your local directory to produce a Gower distance matrix
# dist_matrix <- get_gower_dist("Penguins_Morpho(VarCh)_Extant.nex", numeric = FALSE)
## -----------------------------------------------------------------------------
DataMatrix <- system.file("extdata", "Penguins_Morpho(VarCh)_Extant.nex", package = "EvoPhylo")
dist_matrix <- get_gower_dist(DataMatrix, numeric = FALSE)
## ---- fig.width=6, fig.height=4, fig.align = "center", out.width = "70%"------
## Estimate and plot number of cluster against silhouette width
sw <- get_sil_widths(dist_matrix, max.k = 10)
plot(sw, color = "blue", size = 1)
## ---- fig.width=10, fig.height=7, fig.align = "center", out.width = "100%"----
#User may also generate clusters with PAM and produce a graphic clustering (tSNEs)
clusters <- make_clusters(dist_matrix, k = 3, tsne = TRUE, tsne_dim = 3)
plot(clusters, nrow = 2, max.overlaps = 5)
## ---- eval = FALSE------------------------------------------------------------
# ## Write partitioned alignments to separate Nexus files for BEAUTi
# # Make reference to your original character data matrix in your local directory
# write_partitioned_alignments("Penguins_Morpho(VarCha).nex", clusters, file = "Penguins_Morpho_3p.nex")
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