R: Barcharts and Boxplots for Columns of a Data Matrix Split by...
propBarchart
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.
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.
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$Species)
p
summary(p)
x <- iris[,-5]
x <- cbind(x, Noise=rnorm(150, mean=3))
groupBWplot(x, iris$Species)
groupBWplot(x, iris$Species, shade=TRUE)
groupBWplot(x, iris$Species, shadefun="medianInside")