LIB_RSF {survivalSL} | R Documentation |
Library of the Super Learner for Survival Random Survival Forest
Description
Fit survival random forest tree for given values of the regularization parameters.
Usage
LIB_RSF(formula, data, nodesize, mtry, ntree, 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 whose columns correspond to the variables present in the formula. |
nodesize |
The value of the node size. |
mtry |
The number of variables randomly sampled as candidates at each split. |
ntree |
The number of trees. |
seed |
A random seed to ensure reproducibility during bootstrap sampling. If |
Details
The survival random forest tree is obtained by using the randomForestSRC
package.
Value
formula |
The formula object used for model construction. |
model |
The estimated model. |
data |
The data frame used for learning. |
times |
A vector of numeric values with the times of the |
predictions |
A matrix with the predictions of survivals of each subject (lines) for each observed time (columns). |
References
Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/
Examples
data("dataDIVAT2")
# The estimation of the model
formula<-Surv(times,failures) ~ age + hla + retransplant + ecd
model <- LIB_RSF(formula, data=dataDIVAT2, nodesize=10,
mtry=2, ntree=100, seed=NULL)
# The 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))