survivalSL (version 0.97.1)

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(times, failures, group=NULL, cov.quanti=NULL,
cov.quali=NULL, data, nodesize, mtry, ntree)

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

times

The name of the variable related the numeric vector with the follow-up times.

failures

The name of the variable related the numeric vector with the event indicators (0=right censored, 1=event).

group

The name of the variable related to the exposure/treatment. This variable shall have only two modalities encoded 0 for the untreated/unexposed patients and 1 for the treated/exposed ones. The default value is NULL: no specific exposure/treatment is considered. When a specific exposure/treatment is considered, it will be forced in the algorithm or related interactions will be tested when possible.

cov.quanti

The name(s) of the variable(s) related to the possible quantitative covariates. These variables must be numeric.

cov.quali

The name(s) of the variable(s) related to the possible qualitative covariates. These variables must be numeric with two levels: 0 and 1. A complete disjunctive form must be used for covariates with more levels.

data

A data frame for training the model in which to look for the variables related to the status of the follow-up time (times), the event (failures), the optional treatment/exposure (group) and the covariables included in the previous model (cov.quanti and cov.quali).

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.

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)

tune.model <- tuneRSF(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  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(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  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))