ggRandomForests (version 1.1.1)

pbc_prtl_bili_age: Partial coplot data

Description

Cached partial coplot for bilirubin and age interaction in pbc random forest for survival.

format

A randomForestSRC::plot.variable object for survival

References

Ishwaran H. and Kogalur U.B. (2014). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.5.4.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R. R News 7(2), 25-31.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist. 2(3), 841-860.

Examples

## Examples from randomForestSRC::rfsrc
##
data(pbc, package = "randomForestSRC")
pbc_rf <- rfsrc(Surv(days, status) ~ ., pbc, nsplit = 10)

age_grp <- cut(pbc_rf$xvar$age, breaks=seq(0,100,10))
ggvar$age_grp <- paste("age=",age_grp, sep="")

# Get the training data to work with...
dta.train  <- pbc_rf$xvar
dta.train$age_grp <- age_grp

# Create a series of coplot subsets....
lng <- length(levels(age_grp))
sbst <- mclapply(1:lng, function(ind){
  st <- which(dta.train$age_grp==levels(age_grp)[ind])
  if(length(st) == 0) NULL
  else st
})

lvl <- levels(age_grp)
# Collapse the subset list to interesting items
# (those with observations)
# If you work backwards, you do extra tests, but it
# cuts the correct items. Cute.
for(ind in lng:1){
  if(is.null(sbst[[ind]])){
    sbst[[ind]] <- NULL

    # reset the levels, so we can label things later
    lvl <- lvl[-ind]
  }
}

pDat.partlist <- mclapply(1:length(sbst), function(ind){
  plot.variable(pbc_rf, surv.type="surv", time=1,
                subset = sbst[[ind]],
                xvar.names="bili", partial=TRUE,
                show.plots = FALSE)
})

gg_part <- mclapply(pDat.partlist, gg_partial)

# Flip y-axis
cls <- class(gg_part)
class(gg_part) <-  c("gg_partial_list", cls)

for(ind in 1:length(gg_part)){
  gg_part[[ind]]$age <- lvl[ind]
}
pbc_prtl_bili_age <- do.call(rbind, gg_part)
pbc_prtl_bili_age$age <- paste("Age=", pbc_prtl_bili_age$age)
pbc_prtl_bili_age$age <- factor(pbc_prtl_bili_age$age)

save(pbc_prtl_bili_age, file="data/pbc_prtl_bili_age.rda")

ggpl <- ggplot(gg_merge, aes(x=bili, y=yhat, shape=age, color=age))+
  geom_point()+geom_smooth(se=FALSE)+
  labs(x="Surgical Date", y="Survival 1 year")+
  scale_color_brewer(palette="Set1")
ggpl