stepp.test | R Documentation |
This is a constructor function for the steppes object. In addition, it estimates all the effects of each subpopulations, performs permutation tests and generates all covariance matrices and statistics.
stepp.test(subpop, model, nperm, showstatus = TRUE)
subpop |
the filled stepp subpopulation object |
model |
the model of the data to be used for stepp analysis |
nperm |
number of permutation used in the permutation test |
showstatus |
display the progress bar for the permutation test; default is TRUE |
Permutation tests for all the statistics are done (see ref below). For best results, considering
using 2500 permutations to obtain a rich distribution from which to draw inference.
It returns a steppes object with all effects estimates, covariance matrices and statistics.
Wai-ki Yip
Bonetti M, Zahrieh D, Cole BF, Gelber RD. A small sample study of the STEPP approach to assessing treatment-covariate interactions in survival data. Statistics in Medicine 2009; 28(8):1255-68.
stwin
, stsubpop
, stmodelKM
,
stmodelCI
, stmodelGLM
,
steppes
, stmodel
,
stepp.win
, stepp.subpop
, stepp.KM
,
stepp.CI
, stepp.GLM
,
estimate
, generate
data(bigCI) rxgroup <- bigCI$trt time <- bigCI$time evt <- bigCI$event cov <- bigCI$ki67 # # using constructor functions swin <- stepp.win(type="sliding", r1=50, r2=150) subp <- stepp.subpop(swin=swin, cov=cov) summary(subp) smodel <- stepp.CI(coltrt=rxgroup, trts=c(1,2), coltime=time, coltype=evt, timePoint=4) # Warning: In this example, the permutations have been set to 0 to allow the function # to finish in a short amount of time. IT IS RECOMMEND TO USE AT LEAST 2500 PERMUTATIONS TO # PROVIDE STABLE RESULTS. statCI <- stepp.test(subpop=subp, model=smodel, nperm=0)
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