Survival partial dependence data for one or more predictors

Deprecated. Use gg_partial_rfsrc instead, which returns a classed gg_partial_rfsrc object with a dedicated plot() method.

Usage

surv_partial.rfsrc(rforest, var_list, npts = 25, partial.type = "surv")

Arguments

rforest

A fitted rfsrc survival or competing-risk forest object.

var_list

Character vector of predictor names for which partial dependence should be computed. Each must appear in rforest$xvar.names.

npts

Integer; the number of predictor grid points to evaluate (default 25). Evenly-spaced unique values are sampled from each predictor.

partial.type

The prediction type to return. For survival forests one of "surv" (default), "mort", or "chf". For competing risk forests one of "years.lost", "cif", or "chf". See partial.rfsrc for full details.

Value

A named list with one element per variable in var_list. Each element is itself a list with:

name

The predictor variable name (character).

dta

The raw output of get.partial.plot.data, a list containing at minimum x (predictor values) and yhat (partial predictions), and for survival/competing risk, partial.time.

Details

Computes partial dependence curves for a survival or competing-risk rfsrc forest by calling partial.rfsrc at npts evenly-spaced unique values of each predictor across all stored event times.

See also

gg_partial_rfsrc, partial.rfsrc, get.partial.plot.data

Examples

## ------------------------------------------------------------
## survival
## ------------------------------------------------------------

data(veteran, package = "randomForestSRC")
v.obj <- randomForestSRC::rfsrc(Surv(time,status)~.,
  veteran, nsplit = 10, ntree = 100)

spart <- surv_partial.rfsrc(v.obj, var_list="age", partial.type = "mort")
#> Warning: 'surv_partial.rfsrc' is deprecated and will be removed in a future release.
#> Use 'gg_partial_rfsrc()' instead, which returns a classed object with a
#> dedicated plot() method.
#> partial plot for: age

## partial effect of age on mortality
partial.obj <- partial(v.obj,
                       partial.type = "mort",
                       partial.xvar = "age",
                       partial.values = v.obj$xvar$age,
                       partial.time = v.obj$time.interest)
pdta <- get.partial.plot.data(partial.obj)

plot(lowess(pdta$x, pdta$yhat, f = 1/3),
     type = "l", xlab = "age", ylab = "adjusted mortality")


## example where x is discrete - partial effect of age on mortality
## we use the granule=TRUE option
partial.obj <- partial(v.obj,
                       partial.type = "mort",
                       partial.xvar = "trt",
                       partial.values = v.obj$xvar$trt,
                       partial.time = v.obj$time.interest)
pdta <- get.partial.plot.data(partial.obj, granule = TRUE)
boxplot(pdta$yhat ~ pdta$x, xlab = "treatment", ylab = "partial effect")



## partial effects of karnofsky score on survival
karno <- quantile(v.obj$xvar$karno)
partial.obj <- partial(v.obj,
                       partial.type = "surv",
                       partial.xvar = "karno",
                       partial.values = karno,
                       partial.time = v.obj$time.interest)
pdta <- get.partial.plot.data(partial.obj)

matplot(pdta$partial.time, t(pdta$yhat), type = "l", lty = 1,
        xlab = "time", ylab = "karnofsky adjusted survival")
legend("topright", legend = paste0("karnofsky = ", karno), fill = 1:5)



## ------------------------------------------------------------
## competing risk
## ------------------------------------------------------------

data(follic, package = "randomForestSRC")
follic.obj <- rfsrc(Surv(time, status) ~ ., follic, nsplit = 3, ntree = 100)

## partial effect of age on years lost
partial.obj <- partial(follic.obj,
                       partial.type = "years.lost",
                       partial.xvar = "age",
                       partial.values = follic.obj$xvar$age,
                       partial.time = follic.obj$time.interest)
pdta1 <- get.partial.plot.data(partial.obj, target = 1)
pdta2 <- get.partial.plot.data(partial.obj, target = 2)

# Save and restore the user's graphical parameters per CRAN policy.
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
par(mfrow = c(2, 2))
plot(lowess(pdta1$x, pdta1$yhat),
     type = "l", xlab = "age", ylab = "adjusted years lost relapse")
plot(lowess(pdta2$x, pdta2$yhat),
     type = "l", xlab = "age", ylab = "adjusted years lost death")

## partial effect of age on cif
partial.obj <- partial(follic.obj,
                       partial.type = "cif",
                       partial.xvar = "age",
                       partial.values = quantile(follic.obj$xvar$age),
                       partial.time = follic.obj$time.interest)
pdta1 <- get.partial.plot.data(partial.obj, target = 1)
pdta2 <- get.partial.plot.data(partial.obj, target = 2)

matplot(pdta1$partial.time, t(pdta1$yhat), type = "l", lty = 1,
        xlab = "time", ylab = "age adjusted cif for relapse")
matplot(pdta2$partial.time, t(pdta2$yhat), type = "l", lty = 1,
        xlab = "time", ylab = "age adjusted cif for death")