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(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, 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 |
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 ( |
nodesize |
The value of the node size. |
mtry |
The number of variables randomly sampled as candidates at each split. |
ntree |
The number of trees. |
Details
The survival random forest tree is obtained by using the randomForestSRC
package.
Value
model |
The estimated model. |
group |
The name of the variable related to the exposure/treatment. |
cov.quanti |
The name(s) of the variable(s) related to the possible quantitative covariates. |
cov.quali |
The name(s) of the variable(s) related to the possible qualitative covariates. |
data |
The data frame used for learning. The first column is entitled |
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
model <- LIB_RSF(times="times", failures="failures", data=dataDIVAT2,
cov.quanti=c("age"), cov.quali=c("hla", "retransplant", "ecd"), nodesize=10,
mtry=2, ntree=100)
# 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))