piall: Prediction intervals with all methods

In RFpredInterval: Prediction Intervals with Random Forests and Boosted Forests

Prediction intervals with all methods

Description

Constructs prediction intervals with the 16 methods (PIBF method implemented in pibf() and 15 method variations implemented in rfpi()).

Usage

piall(
  formula,
  traindata,
  testdata,
  alpha = 0.05,
  num.trees = 2000,
  mtry = ceiling(px/3)
)

Arguments

formula	Object of class formula or character describing the model to fit.
traindata	Training data of class data.frame.
testdata	Test data of class data.frame.
alpha	Confidence level. (1 - alpha) is the desired coverage level. The default is alpha = 0.05 for the 95% prediction interval.
num.trees	Number of trees. The default is num.trees = 2000
mtry	Number of variables randomly selected as candidates for splitting a node. The default is rounded up px/3 where px is the number of variables.

Value

A list with the following components:

PIBF	Prediction intervals for test data with PIBF method. A list containing lower and upper bounds.
LS_LM	Prediction intervals for test data with least-squares (LS) splitting rule and classical method (LM). A list containing lower and upper bounds.
LS_SPI	Prediction intervals for test data with least-squares (LS) splitting rule and shortest PI (SPI) method. A list containing lower and upper bounds.
LS_Quant	Prediction intervals for test data with least-squares (LS) splitting rule and quantiles method. A list containing lower and upper bounds.
LS_HDR	Prediction intervals for test data with least-squares (LS) splitting rule and highest density region (HDR) method. A list containing lower and upper bounds of prediction interval for each test observation. There may be multiple PIs for a single observation.
LS_CHDR	Prediction intervals for test data with least-squares (LS) splitting rule and contiguous HDR method. A list containing lower and upper bounds.
L1_LM	Prediction intervals for test data with L_1 splitting rule and classical method (LM). A list containing lower and upper bounds.
L1_SPI	Prediction intervals for test data with L_1 splitting rule and shortest PI (SPI) method. A list containing lower and upper bounds.
L1_Quant	Prediction intervals for test data with L_1 splitting rule and quantiles method. A list containing lower and upper bounds.
L1_HDR	Prediction intervals for test data with L_1 splitting rule and highest density region (HDR) method. A list containing lower and upper bounds of prediction interval for each test observation. There may be multiple PIs for a single observation.
L1_CHDR	Prediction intervals for test data with L_1 splitting rule and contiguous HDR method. A list containing lower and upper bounds.
SPI_LM	Prediction intervals for test data with shortest PI (SPI) splitting rule and classical method (LM). A list containing lower and upper bounds.
SPI_SPI	Prediction intervals for test data with shortest PI (SPI) splitting rule and shortest PI (SPI) method. A list containing lower and upper bounds.
SPI_Quant	Prediction intervals for test data with shortest PI (SPI) splitting rule and quantiles method. A list containing lower and upper bounds.
SPI_HDR	Prediction intervals for test data with shortest PI (SPI) splitting rule and highest density region (HDR) method. A list containing lower and upper bounds of prediction interval for each test observation. There may be multiple PIs for a single observation.
SPI_CHDR	Prediction intervals for test data with shortest PI (SPI) splitting rule and contiguous HDR method. A list containing lower and upper bounds.
pred_pibf	Bias-corrected random forest predictions for test data.
pred_ls	Random forest predictions for test data with least-squares (LS) splitting rule.
pred_l1	Random forest predictions for test data with L_1 splitting rule.
pred_spi	Random forest predictions for test data with shortest PI (SPI) splitting rule.
test_response	If available, true response values of the test data. Otherwise, NULL.

See Also

pibf rfpi plot.rfpredinterval print.rfpredinterval

Examples

## load example data
data(BostonHousing, package = "RFpredInterval")
set.seed(2345)

## define train/test split
testindex <- 1
trainindex <- sample(2:nrow(BostonHousing), size = 50, replace = FALSE)
traindata <- BostonHousing[trainindex, ]
testdata <- BostonHousing[testindex, ]

## construct 95% PI with 16 methods for the first observation in testdata
out <- piall(formula = medv ~ ., traindata = traindata,
  testdata = testdata, num.trees = 50)

RFpredInterval documentation built on March 7, 2023, 7:17 p.m.