flexclust (version 1.3-4)

propBarchart: 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, ...)
"summary"(object, ...)
groupBWplot(x, g, alpha=0.05, correct="holm", col=NULL, shade=!is.null(shadefun), shadefun=NULL, strip.prefix="", strip.labels=NULL, which=NULL, ...)

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.

...

Passed on to barchart or bwplot.

object

Return value of propBarchart.

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.

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)
 
 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")