hclustWidget {MLInterfaces} | R Documentation

## 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)`.

### Details

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

### Value

a shinyApp result that will display in active browser

### Note

`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.

### Author(s)

VJ Carey <stvjc@channing.harvard.edu>

### 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~.))
     }

[Package MLInterfaces version 1.67.10 Index]