print: Print Summary Output of a ltrccif object or a ltrcrrf object

In LTRCforests: Ensemble Methods for Survival Data with Time-Varying Covariates

Print Summary Output of a ltrccif object or a ltrcrrf object

Description

Print summary output after a LTRCCIF or a LTRCRRF model is built. This is the default print method for objects in the class of ltrccif or ltrcrrf.

Usage

print(x)

Arguments

x	an object of class ltrccif or ltrcrrf.

Value

A printout object containing the following components:

Number of (pseudo-subject) observations	number of left-truncated right-censored pseudo-subject observations based on the Andersen-Gill reformulation.
Number of subjects	number of independent subject observations.
Number of deaths	number of times that an event occurs in the whole dataset.
Number of trees	the value set for argument ntree, see ltrccif and ltrcrrf.
minsplit	the value set for argument minsplit that controls the growth of individual trees; see ctree_control.
minbucket	the value set for argument minbucket that controls the growth of individual trees; see ctree_control.
minprob	the value set for argument minprob that controls the growth of individual trees; see ctree_control.
maxdepth	the value set for argument maxdepth that controls the maximum depth of individual trees; see ctree_control.
No. of variables tried at each split	number of input variables randomly sampled as candidates at each node for random forest algorithms, which is either set as an argument mtry in ltrccif and ltrcrrf, or tuned by tune.ltrccif or tune.ltrcrrf, respectively.
Total no. of variables	the number of features provided in data.
Bootstrap type to grow trees	the values set for augument bootstrap, see ltrccif and ltrcrrf.
Resampling used to grow trees	the value set for argument samptype, see ltrccif and ltrcrrf.
Resampling rate used to grow trees	the values set for argument sampfrac, see ltrccif and ltrcrrf.
Analysis	LTRCCIF for a ltrccif object or LTRCRRF for ltrcrrf.
Family	the family used in the analysis, surv.
Splitting rule	the splitting rule that is implemented, conditional inference framework for a ltrccif object or Poisson splitting for ltrcrrf.
Number of random split points	the values set for argument nsplit in ltrcrrf.

See Also

ltrccif, ltrcrrf

Examples

library(survival)
Formula = Surv(Start, Stop, Event) ~ age + alk.phos + ast + chol + edema
# Built a LTRCCIF forest on the time-varying data by specifying id, with mtry specified:
LTRCCIFobj = ltrccif(formula = Formula, data = pbcsample, id = ID, mtry = 3, ntree = 50L)
print(LTRCCIFobj)

# Built a LTRCCIF forest on the time-invariant data, with resampling, with mtry specified:
LTRCCIFobj = ltrccif(formula = Formula, data = pbcsample, samptype = "swr",
                     mtry = 3, ntree = 50L)
print(LTRCCIFobj)

LTRCforests documentation built on May 29, 2024, 7:43 a.m.