mtry to the optimal value with respect to out-of-bag error for a LTRCRRF modelStarting with the default value of mtry, search for the optimal value
(with respect to out-of-bag error estimate) of mtry for ltrcrrf.
tune.ltrcrrf(
formula,
data,
id,
mtryStart = NULL,
stepFactor = 2,
time.eval = NULL,
time.tau = NULL,
ntreeTry = 100L,
bootstrap = c("by.sub", "by.root", "by.node", "by.user", "none"),
samptype = c("swor", "swr"),
sampfrac = 0.632,
samp = NULL,
na.action = "na.omit",
trace = TRUE,
doBest = FALSE,
plot = FALSE,
ntime,
nsplit = 10L,
nodesizeTry = max(ceiling(sqrt(nrow(data))), 15),
nodedepth = NULL
)If doBest = FALSE (default), this returns the optimal mtry value of those searched.
If doBest = TRUE, this returns the ltrcrrf object produced with the optimal mtry.
a formula object, with the response being a Surv
object, with form
Surv(tleft, tright, event).
a a data frame containing n rows of
left-truncated right-censored observations.
variable name of subject identifiers. If this is present, it will be
searched for in the data data frame. Each group of rows in data
with the same subject id represents the covariate path through time of
a single subject. If not specified, the algorithm then assumes data
contains left-truncated and right-censored survival data with time-invariant
covariates.
starting value of mtry; default is sqrt(nvar).
at each iteration, mtry is inflated (or deflated)
by this value, used when mtry is not specified (see ltrcrrf).
The default value is 2.
a vector of time points, at which the estimated survival probabilities are evaluated.
an optional vector, with the i-th entry giving the upper time limit for the
computed survival probabilities for the i-th data (i.e., only computes
survival probabilies at time.eval[time.eval <= time.tau[i]] for the i-th
data of interest).
number of trees used at the tuning step.
bootstrap protocol.
(1) If id is present,
the choices are: "by.sub" (by default) which bootstraps subjects,
"by.root" which bootstraps pseudo-subjects.
Both can be with or without replacement (by default sampling is without
replacement; see the option samptype below).
(2) If id is not specified, the default is "by.root" which
bootstraps the data by sampling with or without replacement;
if "by.node" is choosen, data is bootstrapped with replacement
at each node while growing the tree.
Regardless of the presence of id, if "none" is chosen, the
data is not bootstrapped at all. If "by.user" is choosen,
the bootstrap specified by samp is used.
choices are swor (sampling without replacement) and
swr (sampling with replacement). The default action here is sampling
without replacement.
a fraction, determining the proportion of subjects to draw
without replacement when samptype = "swor". The default value is 0.632.
To be more specific, if id is present, 0.632 * N of subjects with their
pseudo-subject observations are drawn without replacement (N denotes the
number of subjects); otherwise, 0.632 * n is the requested size
of the sample.
Bootstrap specification when bootstype = "by.user".
Array of dim n x ntree specifying how many times each record appears
in each bootstrap sample.
action taken if the data contains NA’s. The default
"na.omit" removes the entire record if any of its entries is
NA (for x-variables this applies only to those specifically listed
in formula). See function rfsrc for
other available options.
whether to print the progress of the search. trace = TRUE is set by default.
whether to run a ltrcrrf object using the optimal mtry found.
doBest = FALSE is set by default.
whether to plot the out-of-bag error as a function of mtry.
plot = FALSE is set by default.
an integer value used for survival to constrain ensemble calculations
to a grid of ntime time points. Alternatively if a vector of values
of length greater than one is supplied, it is assumed these are the time points
to be used to constrain the calculations (note that the constrained time points
used will be the observed event times closest to the user supplied time points).
If no value is specified, the default action is to use all observed event times.
an non-negative integer value for number of random splits to consider
for each candidate splitting variable. This significantly increases speed.
When zero or NULL, the algorithm uses much slower deterministic splitting
where all possible splits are considered. nsplit = 10L by default.
forest average terminal node size used at the tuning step.
maximum depth to which a tree should be grown. The default behaviour is that this parameter is ignored.
sbrier_ltrc for evaluation of model fit for the optimal value of mtry.
### Example with data pbcsample
library(survival)
Formula = Surv(Start, Stop, Event) ~ age + alk.phos + ast + chol + edema
## mtry tuned by the OOB procedure with stepFactor 3, number of trees built 10.
mtryT = tune.ltrcrrf(formula = Formula, data = pbcsample, stepFactor = 3,
ntreeTry = 10L)
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