apc.plot.data.level {apc} | R Documentation |
This plot shows level plot of data matrix based on
levelplot
in the package lattice
.
apc.plot.data.level(apc.data.list,data.type="r", rotate=FALSE,apc.index=NULL, main=NULL,lab=NULL, contour=FALSE,colorkey=TRUE)
apc.data.list |
List. See |
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 |
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 |
main |
Optional. Character. Main title. |
lab |
Optional |
contour |
Optional |
colorkey |
Optional |
Bent Nielsen <bent.nielsen@nuffield.ox.ac.uk> 26 Apr 2015
data.Japanese.breast.cancer
for information on the data used in the example.
##################### # 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)