censIndCR(target, dataset, xIndex, csIndex, dataInfo = NULL, univariateModels = NULL,
hash = FALSE, stat_hash = NULL, pvalue_hash = NULL, robust = FALSE)
censIndWR(target, dataset, xIndex, csIndex, dataInfo = NULL, univariateModels = NULL,
hash = FALSE, stat_hash = NULL, pvalue_hash = NULL, robust = FALSE)
Important: Use these arguments only with the same dataset that was used at initialization.
For all the available conditional independence tests that are currently included on the package, please see "?CondIndTests".
Cox,D.R. (1972) Regression models and life-tables. J. R. Stat. Soc., 34, 187-220.
SES, censIndWR, testIndFisher, gSquare, testIndLogistic, Surv, anova, CondIndTests
#create a survival simulated dataset
dataset <- matrix(runif(1000 * 100, 1, 100), nrow = 1000 , ncol = 100)
dataset <- as.data.frame(dataset);
timeToEvent <- numeric(1000)
event <- numeric(1000)
ca <- numeric(1000)
for(i in 1:1000) {
timeToEvent[i] <- dataset[i, 1] + 0.5*dataset[i, 30] + 2*dataset[i, 65] + runif(1, 0, 1);
event[i] <- sample( c(0, 1), 1)
ca[i] <- runif(1, 0, timeToEvent[i]-0.5)
if(event[i] == 0) {
timeToEvent[i] = timeToEvent[i] - ca[i]
}
}
require(survival, quietly = TRUE)
#init the Surv object class feature
target <- Surv(time = timeToEvent, event = event)
#run the censIndCR conditional independence test
res <- censIndCR( target, dataset, xIndex = 12, csIndex = c(35, 7, 4) )
res
#run the SES algorithm using the censIndCR conditional independence
#test for the survival class variable
sesObject <- SES(target, dataset, max_k = 1, threshold = 0.05, test = "censIndCR");
sesObject2 <- SES(target, dataset, max_k = 1, threshold = 0.05, test = "censIndWR");
#print summary of the SES output
summary(sesObject);
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