subscreenvi: (iii) Determine variable importance

In subscreen: Systematic Screening of Study Data for Subgroup Effects

(iii) Determine variable importance

Description

Determine variable importance for continuous, categorical or right-censored survival endpoints (overall and per treatment group) using random forests

Usage

subscreenvi(data, y, cens = NULL, x = NULL, trt = NULL, ...)

Arguments

data	The data frame containing the dependent and independent variables.
y	The name of the column in data that contains the dependent variable.
cens	The name of the column in data that contains the censoring variable, if y is an event time (default=NULL).
x	Vector that contains the names of the columns in data with the independent variables (default=NULL, i.e. all remaining variables)
trt	The name of the column in data that contains the treatment variable (default=NULL).
...	additional arguments to be passed to function rfsrc

Value

A list containing ordered data frames with the variable importances (one for each treatment level, one with the ranking variability between the treatment levels and one with the total results)

Examples

## Not run: 
require(survival)
data(pbc, package="survival")
# generate categorical versions of some of the baseline covariates
pbc$ageg[!is.na(pbc$age)]        <-
  ifelse(pbc$age[!is.na(pbc$age)]          <= median(pbc$age,     na.rm=TRUE), "Low", "High")
pbc$albuming[!is.na(pbc$albumin)]<-
  ifelse(pbc$albumin[!is.na(pbc$albumin)]  <= median(pbc$albumin, na.rm=TRUE), "Low", "High")
pbc$phosg[!is.na(pbc$alk.phos)]  <-
  ifelse(pbc$alk.phos[!is.na(pbc$alk.phos)]<= median(pbc$alk.phos,na.rm=TRUE), "Low", "High")
pbc$astg[!is.na(pbc$ast)]        <-
  ifelse(pbc$ast[!is.na(pbc$ast)]          <= median(pbc$ast,     na.rm=TRUE), "Low", "High")
pbc$bilig[!is.na(pbc$bili)]      <-
  ifelse(pbc$bili[!is.na(pbc$bili)]        <= median(pbc$bili,    na.rm=TRUE), "Low", "High")
pbc$cholg[!is.na(pbc$chol)]      <-
  ifelse(pbc$chol[!is.na(pbc$chol)]        <= median(pbc$chol,    na.rm=TRUE), "Low", "High")
pbc$copperg[!is.na(pbc$copper)]  <-
  ifelse(pbc$copper[!is.na(pbc$copper)]    <= median(pbc$copper,  na.rm=TRUE), "Low", "High")
pbc$ageg[is.na(pbc$age)]         <- "No Data"
pbc$albuming[is.na(pbc$albumin)] <- "No Data"
pbc$phosg[is.na(pbc$alk.phos)]   <- "No Data"
pbc$astg[is.na(pbc$ast)]         <- "No Data"
pbc$bilig[is.na(pbc$bili)]       <- "No Data"
pbc$cholg[is.na(pbc$chol)]       <- "No Data"
pbc$copperg[is.na(pbc$copper)]   <- "No Data"
#eliminate treatment NAs
pbcdat <- pbc[!is.na(pbc$trt), ]
pbcdat$status <- ifelse(pbcdat$status==0,0,1)
importance <- subscreenvi(data=pbcdat, y='time', cens='status',
 trt='trt', x=c("ageg", "sex", "bilig", "cholg", "copperg"))

## End(Not run)

subscreen documentation built on May 13, 2022, 1:06 a.m.