MLInterfaces (version 1.50.0)

hclustWidget: shiny-oriented GUI for cluster or classifier exploration

Description

shiny-oriented GUI for cluster or classifier exploration

Usage

hclustWidget(mat, featureName = "feature", title = paste0("hclustWidget for ", deparse(substitute(mat))), minfeats = 2, auxdf = NULL)
mlearnWidget(eset, infmla)

Arguments

mat

matrix with feature vectors in rows

featureName

name to be used for control that asks for number of features to use

title

widget title

minfeats

lower bound on number of features to use

auxdf

data.frame with number of rows equal to nrow(mat), with metadata to be displayed in hovering tooltip

eset

instance of ExpressionSet-class

infmla

instance of formula, with dependent variable values obtained in pData(eset), and independent variable specified as "." or as properly munged elements of featureNames(eset).

Value

a shinyApp result that will display in active browser

Details

Experimental tool to illustrate impacts of choice of distance, agglomeration method, etc.

Examples

# should run with example(hclustWidget, ask=FALSE)
if (interactive()) {
 library(shiny)
 library(MASS)
 data(crabs)
 cr = data.matrix(crabs[,-c(1:3)])
 au = crabs[,1:3]
 show(hclustWidget(cr, auxdf=au))
## must use stop widget button to proceed
  library(ALL)
  library(hgu95av2.db)
  data(ALL)
  show(mlearnWidget(ALL[1:500,], mol.biol~.))
 }