survivalSL (version 0.98)

tuneRSF: Tune a Survival Random Forest

Description

This function finds the optimal nodesize, mtry, and ntree parameters for a survival random forest tree.

Usage

tuneRSF(formula, data, nodesize=c(2, 4, 6, 10, 20, 30, 50, 100),
   mtry, ntree=500, seed=NULL)

Value

optimal

The value of lambda that gives the minimum mean cross-validated error.

results

The data frame with the mean cross-validated errors for each lambda values.

Arguments

formula

A formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.

data

A data frame for training the model with the same covariates as in the formula.

nodesize

The values of the node size optimized over.

mtry

The numbers of variables randomly sampled as candidates at each split optimized over.

ntree

The numbers of trees optimized over.

seed

A random seed to ensure reproducibility during the bootstrapping process. If NULL, a seed is randomly assigned.

Details

The function runs the tune.rfsrc function of the randomForestSRC package.

References

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

Examples

data("dataDIVAT2")

formula<-Surv(times,failures) ~ age + hla + retransplant + ecd

tune.model <- tuneRSF(formula, data=dataDIVAT2,
  nodesize=c(100, 250, 500), mtry=1, ntree=100)

tune.model$optimal # the estimated nodesize value

# The estimation of the training modelwith the corresponding lambda value
model <- LIB_RSF(formula, data=dataDIVAT2,
  nodesize=tune.model$optimal$nodesize, mtry=1, ntree=100)

# The resulted predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))