RclusTool (version 0.91)

plotDensity2D: plot Variables Density

Description

Plots density of a variable by cluster.

Usage

plotDensity2D(data, parH = NULL, clustering.name, col = c("grey",
  "black", "red", "blue", "green", "cyan", "yellow", "orange", "rosybrown",
  "palevioletred", "darkblue", "deeppink", "blueviolet", "darkgoldenrod1",
  "chartreuse", "darkorchid1", "deeppink", "coral", "darkolivegreen1",
  "#66C2A5", "#9DAE8C", "#D49A73", "#F08F6D", "#C79693", "#9E9DBA",
  "#9F9BC9", "#C193C6", "#E28BC3", "#D2A29F", "#BABF77", "#AAD852",
  "#CBD844", "#ECD836", "#FAD53E", "#F1CD64", "#E7C689", "#D7BF9C",
  "#C5B9A7", "#B3B3B3", "#D53E4F", "#E04F4A", "#EB6046", "#F47346",
  "#F88B51",      "#FBA35C", "#FDB869", "#FDCA79", "#FDDD88", "#F6E68F",
  "#EDEE93", "#E2F398", "#CDEA9D", "#B7E2A1", "#A0D8A4", "#86CEA4",
  "#6DC4A4", "#58B2AB", "#459DB4", "#3288BD"))

Arguments

data

density data.frame with x,y and Cluster indication.

parH

character vector specifying the name of the feature to use as x-axis.

clustering.name

character vector specifying the name of the clustering.

col

vector of colors

Details

plotDensity2D plots density of a variable by cluster

Examples

# NOT RUN {
dat <- rbind(matrix(rnorm(100, mean = 0, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2))
tf1 <- tempfile()
write.table(dat, tf1, sep=",", dec=".")

x <- importSample(file.features=tf1, dir.save=tempdir())
x <- computeUnSupervised(x, K=3, method.name="K-means")

label<-x[["clustering"]][["K-means_preprocessed"]][["label"]]

cluster.density <- clusterDensity(x, label, "preprocessed",features.to.keep='V1')
plotDensity2D(cluster.density, clustering.name='Test Kmeans', parH='V1')


# }