predict.rfcca: Predict method for rfcca objects

In RFCCA: Random Forest with Canonical Correlation Analysis

Predict method for rfcca objects

Description

Obtain predicted canonical correlations using a rfcca forest for training or new data.

Usage

## S3 method for class 'rfcca'
predict(
  object,
  newdata,
  membership = FALSE,
  finalcca = c("cca", "scca", "rcca"),
  ...
)

Arguments

object	An object of class (rfcca,grow) created by the function rfcca.
newdata	Test data of the set of subject-related covariates (Z). A data.frame with numeric values and factors. If missing, the out-of-bag predictions in object is returned.
membership	Should terminal node membership information be returned?
finalcca	Which CCA should be used for final canonical correlation estimation? Choices are cca, scca and rcca, see rfcca for details. The default is cca.
...	Optional arguments to be passed to other methods.

Value

An object of class (rfcca,predict) which is a list with the following components:

call	The original grow call to rfcca.
n	Sample size of the test data (NA's are omitted). If newdata is missing, sample size of the training set.
ntree	Number of trees grown.
xvar	Data frame of x-variables.
xvar.names	A character vector of the x-variable names.
yvar	Data frame of y-variables.
yvar.names	A character vector of the y-variable names.
zvar	Data frame of test z-variables. If newdata is missing, data frame of training z-variables.
zvar.names	A character vector of the z-variable names.
forest	The (rfcca,grow) forest.
membership	A matrix recording terminal node membership for the test data where each cell represents the node number that an observation falls in for that tree.
predicted	Test set predicted canonical correlations based on the selected final canonical correlation estimation method. If newdata is missing, OOB predictions for training observations.
predicted.coef	Predicted canonical weight vectors for x- and y- variables.
finalcca	The selected CCA used for final canonical correlation estimations.

See Also

rfcca vimp.rfcca print.rfcca

Examples

## load generated example data
data(data, package = "RFCCA")
set.seed(2345)

## define train/test split
smp <- sample(1:nrow(data$X), size = round(nrow(data$X) * 0.7),
  replace = FALSE)
train.data <- lapply(data, function(x) {x[smp, ]})
test.Z <- data$Z[-smp, ]

## train rfcca
rfcca.obj <- rfcca(X = train.data$X, Y = train.data$Y, Z = train.data$Z,
  ntree = 100)

## predict without new data (OOB predictions will be returned)
pred.obj <- predict(rfcca.obj)
pred.oob <- pred.obj$predicted

## predict with new test data
pred.obj2 <- predict(rfcca.obj, newdata = test.Z)
pred <- pred.obj2$predicted

## print predict objects
print(pred.obj)
print(pred.obj2)

RFCCA documentation built on Sept. 19, 2023, 9:06 a.m.