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 returned from a previous call to varpro.

newdata

Optional test data. If not provided, predictions are computed using the training data (out-of-bag).

...

Additional arguments passed to internal methods.

Details

VarPro uses rules extracted from a random forest built using guided tree-splitting, where variables are selected based on split-weights computed in a preprocessing step.

Value

Returns predicted values for the input data. If newdata is provided, predictions are made on that data; otherwise, out-of-bag predictions for the training data are returned.

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

## ------------------------------------------------------------
## toy example - needed to pass CRAN test
## ------------------------------------------------------------

## train call
o <- varpro(mpg~., mtcars[1:20,], ntree = 1)

## predict call
print(predict(o, mtcars[-(1:20),]))


# \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: illustration of hot-encoding 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))

# }