Nothing
data(SimData)
# fit SLCARE with K = 2, formula = "x1 + x2" and default settings for other arguments
model1 <- SLCARE(formula = "x1 + x2", data = SimData, K = 2)
# summary results
summary(model1, digits = 3)
# generate model checking plot
plot(model1, type = "ModelChecking")
# plot estimated cumulative baseline intensity function
plot(model1, type = "mu0")
# generate estimated mean function plot
plot(model1, type = "EstMeans")
# check class membership probabilities of the 6th - 10th subjects in SimData
print(model1, type = "ClassProb")[6:10,]
# check the predicted number of recurrent events of the 6th - 10th subjects in SimData
print(model1, type = "PostPredict")[6:10,]
# check the change in parameter estimates in the last iteration
print(model1, type = "ConvergeLoss")
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