simulatorZ (version 1.6.0)

getTrueModel: getTrueModel

Description

The parametric bootstrap simulation depends on the true model of original sets.

This function is to generate useful values from the true models for further analysis.

We fit CoxBoost to the original sets and use the coefficients to simulate

the survival and censoring time. grid, survH, censH, which are useful for this purpose.

grid=grid corresponding to hazard estimations censH and survH

survH=cumulative hazard for survival times distribution

censH=cumulative hazard for censoring times distribution

Usage

getTrueModel(obj, y.vars, parstep)

Arguments

obj

a list of ExpressionSets, matrix or RangedSummarizedExperiment

y.vars

a list of response variables, Surv, matrix or data.frame object

parstep

number of steps in CoxBoost

Value

beta: True coefficients obtained by fitting CoxBoost to the original ExpressionSetsgrid: timeline grid corresponding to hazard estimations censH and survHsurvH: cumulative hazard for survival times distributioncensH: cumulative hazard for censoring times distributionlp: true linear predictors

Examples

library(curatedOvarianData)


data(GSE17260_eset)


data(E.MTAB.386_eset)


data(GSE14764_eset)


esets <- list(GSE17260=GSE17260_eset, E.MTAB.386=E.MTAB.386_eset, GSE14764=GSE14764_eset)


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


  return(eset[1:500, 1:20])


})





## 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 <- c()


  for(i in seq_along(cens.chr)){


    if(cens.chr[i] == "living") cens[i] <- 1


    else cens[i] <- 0


  }


  y <- Surv(time, cens)


  return(y)


})


   


res1 <- getTrueModel(esets.list, y.list, 100)


## Get true model from one set


res2 <- getTrueModel(esets.list[1], y.list[1], 100)


names(res2)


res2$lp


## note that y.list[1] cannot be replaced by y.list[[1]]