hclustWidget {MLInterfaces} | R Documentation |
shiny-oriented GUI for cluster or classifier exploration
hclustWidget(mat, featureName = "feature", title = paste0("hclustWidget for ", deparse(substitute(mat))), minfeats = 2, auxdf = NULL) mlearnWidget(eset, infmla)
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 |
infmla |
instance of |
Experimental tool to illustrate impacts of choice of distance, agglomeration method, etc.
a shinyApp result that will display in active browser
mlearnWidget
will attempt to nicely produce a variable
importance plot using randomForestI
. This means
that the annotation package for probe identifiers should be loaded
or an error will be thrown.
VJ Carey <stvjc@channing.harvard.edu>
# 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~.)) }