cox.predict.Rd
This function takes a fitted Cox model, i.e., a model estimated by opt_Cox
or 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.
cox_predict(object, newdata, type = c("link", "parameter", "probabilities"), FUN = function(x) { mean(x, na.rm = TRUE) }, time = NULL, subdivisions = 100, cores = NULL, chunks = 1, verbose = FALSE, ...)
object  A 

newdata  A data frame or list containing the values of the model
covariates at which predictions are required. If missing 
type  Specifies the type of predictions that should be computed. 
FUN  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 
time 

subdivisions  How many time points should be created for each individual. 
cores  Specifies the number of cores that should be used for prediction. The problem is
split into 
chunks  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. 
verbose  Print progress information. 
…  Arguments passed to 
Depending on the type of function provided in argument FUN
, a numeric
vector
or matrix
.
# NOT RUN { 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) ## Predict survival probabilities P(T > t). p < predict(b, type = "probabilities", time = 3, subdivisions = 100, FUN = c95) # }