- 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
.