Description Usage Arguments Value References Examples
The function funest_fit takes a long and a short form of the survival data, among other arguments for a random survival forest, to fit an functional ensemble survival tree model for predicting survival probability.
1 2 3 4 5 6 7 8 9 10 11 12 13 | funest_fit(
long_train,
surv_train,
noftree = 500,
nofcov = 2,
split_rule = "maxstat",
tv_names,
fv_names,
nofp = 3,
t_star,
t_pred,
...
)
|
long_train |
long form of survival data from the training set |
surv_train |
short form of survival data from the training set |
noftree |
number of trees in the random survival forest |
nofcov |
number of covariates selected in each survival tree |
split_rule |
binary splitting rule for random survival forest, default is "maxstat" |
tv_names |
a list of names of time-varying covariates |
fv_names |
a list of names of fixed covariates |
nofp |
number of multivariate principal components |
t_star |
time for the last observed biomarker measurement |
t_pred |
time at prediction |
... |
extra arguments that can be passed to ranger() |
A list compose two items. The first item is a list of necessary information for prediction used in funest_pred() function. The second item is the ranger object of the fitted random survival forest.
misc - a list composed of 1) long_train: long form of survival data from the training set, 2) surv_train: short form of survival data from the training set, 3) fmla: covariates passed into the ensemble survival tree 4) score_names: intermediate names for the covariates 5) nofp: number of multivariate principal components 6) train_data.sub: data frame of all covariates after MFPCA been performed
rg - functional ensemble survival tree model
nestpaperfunest
\insertRefrangerfunest
1 2 3 4 5 | library(funest)
data("long_train")
data("surv_train")
w = funest_fit(long_train, surv_train, tv_names = list("Y1", "Y2", "Y3"), fv_names = list("W"),
noftree = 10, t_star = 5.5, t_pred = 11)
|
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