apc (version 2.0.0)

apc.plot.data.level: Level plot of data matrix.

Description

This plot shows level plot of data matrix based on levelplot in the package lattice.

Usage

apc.plot.data.level(apc.data.list,data.type="r",
 						rotate=FALSE,apc.index=NULL,
						main=NULL,lab=NULL,
						contour=FALSE,colorkey=TRUE)

Arguments

apc.data.list

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

data.type

Optional. Character. "r"="response" / "d"="dose" / "m"="mortality"="rates" if sums are computed for responses/dose/rates, where rates are found through division response/dose. It also takes data types "residual" / "fitted.values" / "linear.predictors" when the argument apc.data.list is the output of the fitting function apc.fit.model, which is an extended apc.data.list. "r" is default.

rotate

Optional. Logical. If TRUE rotates plot 90 degrees clockwise (or anti-clockwise if data.format is "CL"). Default is FALSE.

apc.index

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

main

Optional. Character. Main title.

lab

Optional plot parameter. A numerical vector of the form c(x, y, len) which modifies the default way that axes are annotated. The values of x and y give the (approximate) number of tickmarks on the x and y axes. len is not implemented.

contour

Optional levelplot (lattice) parameter. Logical. Contour lines drawn if TRUE. Default FALSE.

colorkey

Optional levelplot (lattice) parameter. Logical or list. Determines color key. Default TRUE.

See Also

data.Japanese.breast.cancer for information on the data used in the example.

Examples

# NOT RUN {
#####################
#	EXAMPLE with Japanese breast cancer data
#	Clayton and Shifflers (1987b) use APC design
#	Make a data list
#	Then plot data.
#	Note: No plot appears to have approximately parallel lines.

data.list	<- data.Japanese.breast.cancer()
apc.plot.data.level(data.list,"r")
dev.new()
apc.plot.data.level(data.list,"d",contour=TRUE)
												
#	It also works with a single argument, but then a default log scale is used.
# 	Note that warnings are given in relation to the data chosen thinning

apc.plot.data.within(data.list)

#####################
#	EXAMPLE with Italian bladder cancer data
#	Clayton and Shifflers (1987a) use AC design
#	Note: plot of within cohort against age appears to have approximately parallel lines.
#		  This is Figure 2 in Clayton and Shifflers (1987a)
#	Note: plot of within age against cohort appears to have approximately parallel lines.
#		  Indicates that interpretation should be done carefully.

data.list	<- data.Italian.bladder.cancer()	
apc.plot.data.within(data.list,"m",1,log="y")

#####################
#	EXAMPLE with asbestos data
#	Miranda Martinex, Nielsen and Nielsen (2014).
#	This is Figure 1d 

data.list	<- data.asbestos()	
apc.plot.data.within(data.list,type="l",lty=1)
# }