tune.rf.time: Tune Survival Random Forest Tree

In RISCA: Causal Inference and Prediction in Cohort-Based Analyses

Tune Survival Random Forest Tree

Description

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

Usage

tune.rf.time(times, failures, group, cov.quanti,
cov.quali, data, cv, nodesize, mtry, ntree)

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).
cv	The value of the number of folds. The default value is 10.
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.

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.

Author(s)

Yohann Foucher <[e-mail]>

Camille Sabathe <[e-mail]>

References

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

Examples

data(dataDIVAT2)

tune.model<-tune.rf.time(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), cv=5,
  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<-rf.time(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))
     

RISCA documentation built on March 31, 2023, 11:06 p.m.