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

print {LTRCforests}

R Documentation

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`.

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)