VisualOptClusters: VisualOptClusters

In Sautie/MALDITOFSpectraPA: Processing and analysis of MALDI_TOF spectra

Description

visualization and statistics for clustering validation

Usage

VisualOptClusters(
  df_m,
  meth = "hclust",
  meth2 = "ward.D2",
  dist = "euclidean",
  varCat1,
  value,
  nc = 3
)

Arguments

df_m:	dataframe containing peaks and metadata
meth:	clustering algorithms ("hclust", default value), other values: "kmeans", "pam", "clara", "fanny", "hclust","agnes", "diana"
dist:	distances ("euclidean", default value), "maximum", "manhattan", "canberra", "binary" "minkowski"
meth2:	hc methods ("ward.D2", default value), average", "ward.D", "single", "complete", "mcquitty", "median" or "centroid"
varCat1:	categorical variable for choosing isolates, examples: "Taxonomie" ,"Genre", "Date.d.analyse" ,"Origine","Ruche", "Nutrition" , "Date.de.récolte" , "Lieu.de.la.ruche"
value:	level of catVar1, examples: "Lactobacillus" ("Genre"), Taxonomie("Pediococcus pentosaceus"), "Erica cinerea" ("Nutrition"),...
nc:	number of clusters (nc=3, default value)

Value

figures and statistics

Examples

       s<-VisualOptClusters(df_Peaks, varCat1="Genre", value="Enterococcus"),
                 s<-VisualOptClusters(df_Peaks, meth="pam", varCat1="Genre", value="Lactobacillus", nc=2)
                s<-VisualOptClusters(df_Peaks, meth="hclust", meth2="average", dist="pearson",varCat1="Genre", value="All", nc=5),
                s<-VisualOptClusters(df_Peaks, meth="kmeans", dist="euclidean",varCat1="Genre", value="All", nc=3)
       source:  https://www.rdocumentation.org/packages/factoextra/versions/1.0.7/topics/fviz_cluster
                https://afit-r.github.io/kmeans_clustering
                http://rstudio-pubs-static.s3.amazonaws.com/265632_3ad9e0b981244e15887677f8dffb39a0.html#using-30-different-indices

Sautie/MALDITOFSpectraPA documentation built on Dec. 31, 2020, 4:28 p.m.