tuneRSF {survivalSL} | R Documentation |
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)
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 |
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 |
Details
The function runs the tune.rfsrc
function of the randomForestSRC
package.
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. |
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))