simTime: simTime

In simulatorZ: Simulator for Collections of Independent Genomic Data Sets

Description

simTime is a function to perform the parametric-bootstrap step, where we use the true coefficients

and cumulative hazard to simulate survival and censoring.

Usage

simTime(simmodels, original.yvars, result)

Arguments

simmodels	a list in the form of the return value of simData() which consists of three lists: obj: a list of ExpressionSets, matrices or RangedSummarizedExperiments setsID: a list of set labels indicating which original set the simulated one is from indices: a list of patient labels to tell which patient in the original set is drawn
original.yvars	response variable in the order of original sets(without sampling)
result	a list in the form of return of getTrueModel() which consists of five lists: Beta: a list of coefficients obtained by grid: timeline grid corresponding to hazard estimations censH and survH survH: cumulative hazard for survival times distribution censH: cumulative hazard for censoring times distribution lp: true linear predictors

Value

survival time is saved in phenodata, here the function still returns the ExpressionSets

Author(s)

Yuqing Zhang, Christoph Bernau, Levi Waldron

Examples

library(curatedOvarianData)


data(E.MTAB.386_eset)


data(GSE14764_eset)


esets.list <- list(E.MTAB.386=E.MTAB.386_eset[1:100, 1:20], GSE14764=GSE14764_eset[1:100, 1:20])


rm(E.MTAB.386_eset, GSE14764_eset)





## simulate on multiple ExpressionSets


set.seed(8) 





y.list <- lapply(esets.list, function(eset){


  time <- eset$days_to_death


  cens.chr <- eset$vital_status


  cens <- rep(0, length(cens.chr))


  cens[cens.chr=="living"] <- 1


  return(Surv(time, cens))


})





# To perform both parametric and non-parametric bootstrap, you can call simBootstrap()


# or, you can divide the steps into:


res <- getTrueModel(esets.list, y.list, 100)


simmodels <- simData(obj=esets.list, y.vars=y.list, n.samples=10)





# Then, use this function


simmodels <- simTime(simmodels=simmodels, original.yvars=y.list, result=res) 





# it also supports performing only the parametrc bootstrap step on a list of expressionsets


# but you need to construct the parameter by scratch


res <- getTrueModel(esets.list, y.list, 100)


setsID <- seq_along(esets.list)


indices <- list()


for(i in setsID){


  indices[[i]] <- seq_along(sampleNames(esets.list[[i]])) 


}


simmodels <- list(obj=esets.list, y.vars=y.list, indices=indices, setsID=setsID)





new.simmodels <- simTime(simmodels=simmodels, original.yvars=y.list, result=res)  

simulatorZ documentation built on Nov. 8, 2020, 5 p.m.