apc.plot.data.sparsity {apc}

This plot shows heat map of the sparsity of a data matrix.

Description

The plot shows where the data matrix is sparse.

Usage

apc.plot.data.sparsity(apc.data.list,
						data.type="a",swap.axes=FALSE,
						apc.index=NULL,
						sparsity.limits=c(1,2),
						cex=NULL,pch=15,
						main.outer=NULL)

Arguments

apc.data.list

List. See apc.data.list for a description of the format.

data.type

Optional. Character. "r"/"d"/"m" if sums are computed for responses/dose/all. "r" is default.

swap.axes

Optional. Logical. If true swap axes in plot. Default is FALSE unless data.format="CL"

apc.index

Optional. List. See apc.get.index for a description of the format. If not provided this is computed.

sparsity.limits

Optional. vector with two values in increasing order. Default is c(1,2). The sparsity plot is a heat map with three colours: black if the observation is smaller than first index (default 1), grey if the observation is smaller than the second index (default 2) and otherwise white.

cex

Optional plot argument. A numerical value giving the amount by which plotting text and symbols should be magnified. Default is NULL in which case program chooses.

pch

Optional. vector with two values. Either integers specifying a symbol or characters. See points for possible values and their interpretation. Default is c(15,15), which is filled square.

main.outer

Optional. Character. Main title for plot, to be shown in outer margin. Default is NULL, in which case a title is generated internally.

Details

The default values is used to highlight where a matrix of counts has values of zero and one. Estimation can be very noise in those areas.

Note

Note that the axes for plots grow from bottom left while axes for matrices grow from top left. The exception is when data.format="CL", in which case both grow from top left.

Author(s)

Bent Nielsen <[e-mail]> 25 Apr 2015 updated 27 Apr 2015

See Also

The example below uses asbestos data, see data.asbestos

Examples

#####################
#  EXAMPLE with artificial data
#  generate a 3x4 matrix in "AP" data.format with the numbers 1..12

m.data  	<- matrix(data=seq(length.out=12),nrow=3,ncol=4)
m.data
data.list	<- apc.data.list(m.data,"AP")
apc.plot.data.sparsity(data.list)

#####################
#	EXAMPLE with Japanese breast cancer data
#	get data list, then make sparsity plots.

data.list	<- data.asbestos()					
apc.plot.data.sparsity(data.list)