clusterSummary: Clusters summaries computation

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

Clusters summaries computation

Description

Save clusters summaries results in a csv file.

Usage

clusterSummary(
  data.sample,
  label,
  features.to.keep = colnames(data.sample$features[["preprocessed"]]$x),
  summary.functions = c(Min = "min", Max = "max", Sum = "sum", Average = "mean", SD =
    "sd")
)

Arguments

data.sample	list containing features, profiles and clustering results.
label	vector of labels.
features.to.keep	vector of features names on which the summaries are computed.
summary.functions	vector of functions names for the summaries computation. Could be 'Min', 'Max', 'Sum', 'Average', 'sd'.

Details

clusterSummary computes the clusters summaries (min, max, sum, average, sd) from a clustering result.

Value

out data.frame containing the clusters summaries.

Examples

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)
res <- KmeansQuick(x$features$initial$x, K=3)
labels <- formatLabelSample(res$cluster, x)
cluster.summary <- clusterSummary(x, labels)

RclusTool documentation built on Aug. 29, 2022, 9:07 a.m.