getTrueModel: getTrueModel

Description Usage Arguments Value Author(s) Examples

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

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getTrueModel(esets, y.vars, parstep, balance.variables = NULL)

Arguments

esets

a list of ExpressionSets, matrix or SummarizedExperiment

y.vars

a list of response variables

parstep

CoxBoost parameter

balance.variables

variable names to be balanced.

Value

returns a list of values:

beta: True coefficients obtained by fitting CoxBoost to the original ExpressionSets

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

Author(s)

Yuqing Zhang, Christoph Bernau, Levi Waldron

Examples

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library(curatedOvarianData)


data(GSE14764_eset)


data(E.MTAB.386_eset)


esets.list <- list(GSE14764=GSE14764_eset[1:500, 1:20], 


                   E.MTAB.386=E.MTAB.386_eset[1:500, 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))


})


   


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]]

simulatorZ documentation built on Nov. 1, 2018, 2:25 a.m.