This function takes a fitted Cox model, i.e., a model estimated by
sam_Cox and computes predictions given a new data set or the
original values. Survival probabilities are computed using numerical integration, therefore,
computation may take some time. To avoid problems with computer memory, the prediction
of survival probabilities can be split into chunks and computed parallel on different cores.
A data frame or list containing the values of the model
covariates at which predictions are required. If missing
newdata is the
model.frame of the provided model.
Specifies the type of predictions that should be computed.
A function that should be applied on each row of the samples
of the additive predictor, parameter or probabilities. Per default
the function computes means of samples, however, other functions like
can be supplied.
numeric, specifies the time for which survival probabilities should
be computed if
type = "probabilities". Note that this overwrites survival times
that are supplied in argument
How many time points should be created for each individual.
Specifies the number of cores that should be used for prediction. The problem is
core chunks, each chunk is then processed by one core.
The number of chunks that should be processed sequentially on one core. This way memory problems can be avoided when computing survival times for large problems.
Print progress information.
Arguments passed to
Depending on the type of function provided in argument
if (FALSE) library("survival") set.seed(123) ## Simulate survival data. d <- simSurv(n = 500) ## Formula of the survival model, note ## that the baseline is given in the first formula by s(time). f <- list( Surv(time, event) ~ s(time) + s(time, by = x3), gamma ~ s(x1) + s(x2) ) ## Cox model with continuous time. ## Note the the family object cox_bamlss() sets ## the default optimizer and sampler function! ## First, posterior mode estimates are computed ## using function opt_Cox(), afterwards the ## sampler sam_Cox() is started. b <- bamlss(f, family = "cox", data = d) #> Error in Surv(time, event): could not find function "Surv" ## Predict survival probabilities P(T > t). p <- predict(b, type = "probabilities", time = 3, subdivisions = 100, FUN = c95) #> Error in predict(b, type = "probabilities", time = 3, subdivisions = 100, FUN = c95): object 'b' not found