piall {RFpredInterval} | R Documentation |
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 |
traindata |
Training data of class |
testdata |
Test data of class |
alpha |
Confidence level. (1 - |
num.trees |
Number of trees. The default is |
mtry |
Number of variables randomly selected as candidates for splitting
a node. The default is rounded up |
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 |
L1_SPI |
Prediction intervals for test data with |
L1_Quant |
Prediction intervals for test data with |
L1_HDR |
Prediction intervals for test data with |
L1_CHDR |
Prediction intervals for test data with |
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 |
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, |
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)