prediction using RLT
predict.RLT.Rd
Predict the outcome (regression, classification or survival) using a fitted RLT object
Usage
# S3 method for class 'RLT'
predict(
object,
testx = NULL,
var.est = FALSE,
keep.all = FALSE,
ncores = 1,
verbose = 0,
...
)
Arguments
- object
A fitted RLT object
- testx
The testing samples, must have the same structure as the training samples
- var.est
Whether to estimate the variance of each testing data. The original forest must be fitted with
var.ready = TRUE
. For survival forests, calculates the covariance matrix over all observed time points and calculates critical value for the confidence band.- keep.all
whether to keep the prediction from all trees. Warning: this can occupy a large storage space, especially in survival model
- ncores
number of cores
- verbose
print additional information
- ...
...
Value
A RLT
prediction object, constructed as a list consisting
- Prediction
Prediction
- Variance
if
var.est = TRUE
and the fitted object isvar.ready = TRUE
For Survival Forests
- hazard
predicted hazard functions
- CumHazard
predicted cumulative hazard function
- Survival
predicted survival function
- Allhazard
if
keep.all = TRUE
, the predicted hazard function for each observation and each tree- AllCHF
if
keep.all = TRUE
, the predicted cumulative hazard function for each observation and each tree- Cov
if
var.est = TRUE
and the fitted object isvar.ready = TRUE
. For each test subject, a matrix of size NFail\(\times\)NFail where NFail is the number of observed failure times in the training data- Var
if
var.est = TRUE
and the fitted object isvar.ready = TRUE
. Marginal variance for each subject- timepoints
ordered observed failure times from the training data
- MarginalVar
if
var.est = TRUE
and the fitted object isvar.ready = TRUE
. Marginal variance for each subject from the Cov matrix projected to the nearest positive definite matrix- MarginalVarSmooth
if
var.est = TRUE
and the fitted object isvar.ready = TRUE
. Marginal variance for each subject from the Cov matrix projected to the nearest positive definite matrix and then smoothed using Gaussian kernel smoothing- CVproj
if
var.est = TRUE
and the fitted object isvar.ready = TRUE
. Critical values to calculate confidence bands around cumulative hazard predictions at several confidence levels. Calculated usingMarginalVar
- CVprojSmooth
if
var.est = TRUE
and the fitted object isvar.ready = TRUE
. Critical values to calculate confidence bands around cumulative hazard predictions at several confidence levels. Calculated usingMarginalVarSmooth