R: Barcharts and Boxplots for Columns of a Data Matrix Split by...

propBarchart {flexclust} | R Documentation

## Barcharts and Boxplots for Columns of a Data Matrix Split by Groups

### Description

Split a binary or numeric matrix by a grouping variable, run a series of tests on all variables, adjust for multiple testing and graphically represent results.

### Usage

    propBarchart(x, g, alpha=0.05, correct="holm", test="prop.test",
                 sort=FALSE, strip.prefix="", strip.labels=NULL,
                 which=NULL, byvar=FALSE, ...)

    ## S4 method for signature 'propBarchart'
    summary(object, ...)

    groupBWplot(x, g, alpha=0.05, correct="holm", xlab="", col=NULL,
                shade=!is.null(shadefun), shadefun=NULL,
                strip.prefix="", strip.labels=NULL, which=NULL, byvar=FALSE,
                ...)

### Arguments

`x` | A binary data matrix.
`g` | A factor specifying the groups.
`alpha` | Significance level for test of differences in proportions.
`correct` | Correction method for multiple testing, passed to `p.adjust`.
`test` | Test to use for detecting significant differences in proportions.
`sort` | Logical, sort variables by total sample mean?
`strip.prefix` | Character string prepended to strips of the `barchart` (the remainder of the strip are group levels and group sizes). Ignored if `strip.labels` is specified.
`strip.labels` | Character vector of labels to use for strips of `barchart`.
`which` | Index numbers or names of variables to plot.
`byvar` | If `TRUE`, a panel is plotted for each variable. By default a panel is plotted for each group.
`...` | Passed on to `barchart` or `bwplot`.
`object` | Return value of `propBarchart`.
`xlab` | A title for the x-axis: see `title`. The default is `""`.
`col` | Vector of colors for the panels.
`shade` | If `TRUE`, only variables with significant differences in median are filled with color.
`shadefun` | A function or name of a function to compute which boxes are shaded, e.g. `"kruskalTest"` (default), `"medianInside"` or `"boxOverlap"`.

### Details

Function `propBarchart` splits a binary data matrix into subgroups, computes the percentage of ones in each column and compares the proportions in the groups using `prop.test`. The p-values for all variables are adjusted for multiple testing and a barchart of group percentages is drawn highlighting variables with significant differences in proportion. The `summary` method can be used to create a corresponding table for publications.

Function `groupBWplot` takes a general numeric matrix, also splits into subgroups and uses boxes instead of bars. By default `kruskal.test` is used to compute significant differences in location, in addition the heuristics from `bwplot,kcca-method` can be used. Boxes of the complete sample are used as reference in the background.

### Author(s)

Friedrich Leisch

### See Also

`barplot-methods`, `bwplot,kcca-method`

### Examples

     ## create a binary matrix from the iris data plus a random noise column
     x <- apply(iris[,-5], 2, function(z) z>median(z))
     x <- cbind(x, Noise=sample(0:1, 150, replace=TRUE))

     ## There are significant differences in all 4 original variables, Noise
     ## has most likely no significant difference (of course the difference
     ## will be significant in alpha percent of all random samples).
     p <- propBarchart(x, iris$Species)
     p
     summary(p)
     propBarchart(x, iris$Species, byvar=TRUE)

     x <- iris[,-5]
     x <- cbind(x, Noise=rnorm(150, mean=3))
     groupBWplot(x, iris$Species)
     groupBWplot(x, iris$Species, shade=TRUE)
     groupBWplot(x, iris$Species, shadefun="medianInside")
     groupBWplot(x, iris$Species, shade=TRUE, byvar=TRUE)

[Package flexclust version 1.4-0 Index]