Prediction on Test Data using VarPro

Obtain predicted values on test data for VarPro object.

# S3 method for class 'varpro'
predict(object, newdata, ...)

Arguments

object

VarPro object obtained from previous call to varpro.

newdata

Test data. If not provided the training data is used.

...

Additional options.

Details

The VarPro method uses the rules obtained from a random forest constructed by guided tree-splitting where variables are selected according to a split-weight obtained in a pre-processing step.

This function returns the predicted values for this random forest on the provided test data, newdata (if not provided, predicted values are the out-of-bag predicted values for the training data).

Author

Min Lu and Hemant Ishwaran

References

Lu, M. and Ishwaran, H. (2024). Model-independent variable selection via the rule-based variable priority. arXiv e-prints, pp.arXiv-2409.

See also

varpro

Examples

# \donttest{
## ------------------------------------------------------------
##
## boston housing regression
## obtain predicted values for the training data
##
## ------------------------------------------------------------

## varpro applied to boston housing data
data(BostonHousing, package = "mlbench")
o <- varpro(medv~., BostonHousing)

## predicted values for the training features
print(head(predict(o)))

## ------------------------------------------------------------
##
## iris classification
## obtain predicted values for test data
##
## ------------------------------------------------------------

## varpro applied to iris data
trn <- sample(1:nrow(iris), size = 100, replace = FALSE)
o <- varpro(Species~., iris[trn,])

## predicted values on test data
print(data.frame(Species=iris[-trn, "Species"], predict(o, iris[-trn,])))

## ------------------------------------------------------------
##
## mtcars regression
## obtain predicted values for test data
## also illustrates hot-encoding working on test data
##
## ------------------------------------------------------------

## mtcars with some factors
d <- data.frame(mpg=mtcars$mpg,lapply(mtcars[, c("cyl", "vs", "carb")], as.factor))

## varpro on training data 
o <- varpro(mpg~., d[1:20,])

## predicted values on test data
print(predict(o, d[-(1:20),]))

## predicted values on bad test data with strange factor values 
dbad <- d[-(1:20),]
dbad$carb <- as.character(dbad$carb)
dbad$carb <-  sample(LETTERS, size = nrow(dbad))
print(predict(o, dbad))

# }