hs_hclust: Perform hierarchical clustering on Raman spectra

In CMET-UGent/MicroRaman: Phenotyping of microbial Raman spectroscopy data

Description

Perform hierarchical clustering on Raman spectra

Usage

hs_hclust(
  hs.x,
  dist_method = "bray",
  clust_method = "pvclust",
  aggl_method = "ward.D2",
  nboot = 1000,
  ...
)

Arguments

hs.x	hyperSpec object
dist_method	Dissimilarity index, partial match to "manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao" or "mahalanobis". Can be specified to "SCA" for Raman data.
clust_method	Choose regular hierarchical clustering or bootstrap supported hierarchical clustering with pvclust. Defaults to pvclust
aggl_method	the agglomerative method used in hierarchical clustering. This should be (an abbreviation of) one of "average", "ward.D", "ward.D2", "single", "complete", "mcquitty", "median" or "centroid". The default is "average".
nboot	Numnber of bootstraps for pvclust. Defaults to 1000.
...	Parameters to pass on to hclust().

Examples

## Short example

# Load hyperSpec object
data("hs_example")

# Preprocess spectra
hs.x.proc <- hs_preprocess(hs_example)
hs.x.proc <- hs_resample(hs.x.proc, sample = 10)

# Cluster
hclust_obj <- hs_hclust(hs.x.proc, dist_method = "manhattan",
clust_method = "pvclust")

CMET-UGent/MicroRaman documentation built on July 25, 2020, 6:20 p.m.