Package 'RFpredInterval' reference manual

Title:	Prediction Intervals with Random Forests and Boosted Forests
Description:	Implements various prediction interval methods with random forests and boosted forests. The package has two main functions: pibf() produces prediction intervals with boosted forests (PIBF) as described in Alakus et al. (2021) <arXiv:2106.08217> and rfpi() builds 15 distinct variations of prediction intervals with random forests (RFPI) proposed by Roy and Larocque (2020) <doi:10.1177/0962280219829885>.
Authors:	Cansu Alakus [aut, cre], Denis Larocque [aut], Aurelie Labbe [aut], Hemant Ishwaran [ctb] (Author of included randomForestSRC codes), Udaya B. Kogalur [ctb] (Author of included randomForestSRC codes)
Maintainer:	Cansu Alakus <[email protected]>
License:	GPL (>= 3)
Version:	1.0.6
Built:	2024-11-05 04:43:19 UTC
Source:	https://github.com/calakus/rfpredinterval

RFpredInterval: A package for building prediction intervals with random forests and boosted forests

Description

RFpredInterval provides methods to build prediction intervals with random forests. The methods provided in the package are Prediction Intervals with Boosted Forests (PIBF) proposed by Alakus et al. (2021) and 15 distinct variations to build PIs proposed by Roy and Larocque (2020). RFpredInterval includes two main functions: pibf() and rfpi(). pibf() applies the PIBF method and it uses the ranger package (Wright and Ziegler, 2017) to fit random forests. rfpi() applies the 15 variations proposed by Roy and Larocque (2020). For rfpi(), RFpredInterval uses randomForestSRC package. For the least-squares splitting rule, both randomForestSRC and ranger packages are applicable.

Details

Among 16 methods, ten of them have specialized splitting rules in the random forest growing process. These methods are the ones with L1 and shortest prediction interval (SPI) splitting rules proposed by Roy and Larocque (2020). To implement these methods, the custom split feature of the randomForestSRC package (Ishwaran and Kogalur, 2021) have been utilised.

The randomForestSRC package allows users to define a custom splitting rule for the tree growing process. The user needs to define the customized splitting rule in the splitCustom.c file. After modifying the splitCustom.c file, all C source code files under the src folder of the package must be recompiled. Finally, the package must be re-installed for the custom split rule to become available. RFpredInterval uses randomForestSRC package by freezing at the version 2.11.0.

RFpredInterval functions

pibf rfpi piall plot.rfpredinterval print.rfpredinterval

References

Alakus, C., Larocque, D., and Labbe, A. (2021). RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests. arXiv preprint arXiv:2106.08217.

Ishwaran H, Kogalur U (2021). Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). R package version 2.11.0, https://cran.r-project.org/package=randomForestSRC.

Roy, M. H., & Larocque, D. (2020). Prediction intervals with random forests. Statistical methods in medical research, 29(1), 205-229. doi:10.1177/0962280219829885.

Wright MN, Ziegler A (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1–17. doi:10.18637/jss.v077.i01.

Boston housing data set

Description

Housing data for 506 census tracts of Boston from the 1970 census. The data set contains the original data by Harrison and Rubinfeld (1979).

Usage

BostonHousing
BostonHousing

Format

A data frame with three 506 rows observations on 14 variables. medv is the target variable. The variables are as follows:

crim: per capita crime rate by town
zn: proportion of residential land zoned for lots over 25,000 sq.ft
indus: proportion of non-retail business acres per town
chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
nox: nitric oxides concentration (parts per 10 million)
rm: average number of rooms per dwelling
age: proportion of owner-occupied units built prior to 1940
dis: weighted distances to five Boston employment centres
rad: index of accessibility to radial highways
tax: full-value property-tax rate per USD 10,000
ptratio: pupil-teacher ratio by town
b: 1000(B - 0.63)^2 where B is the proportion of blacks by town
lstat: percentage of lower status of the population
medv: median value of owner-occupied homes in USD 1000's

Examples

## load data
data(BostonHousing, package = "RFpredInterval")
## load data
data(BostonHousing, package = "RFpredInterval")

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)
)
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`.

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)


## 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)

Prediction intervals with boosted forests

Description

Constructs prediction intervals with boosted forests.

Usage

pibf(
  formula,
  traindata,
  testdata,
  alpha = 0.05,
  calibration = c("cv", "oob", FALSE),
  coverage_range = c(1 - alpha - 0.005, 1 - alpha + 0.005),
  numfolds = 5,
  params_ranger = list(num.trees = 2000, mtry = ceiling(px/3), min.node.size = 5,
    replace = TRUE),
  oob = FALSE
)
pibf(
  formula,
  traindata,
  testdata,
  alpha = 0.05,
  calibration = c("cv", "oob", FALSE),
  coverage_range = c(1 - alpha - 0.005, 1 - alpha + 0.005),
  numfolds = 5,
  params_ranger = list(num.trees = 2000, mtry = ceiling(px/3), min.node.size = 5,
    replace = TRUE),
  oob = FALSE
)

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.
`calibration`	Calibration method for finding working level of `alpha`, i.e. $\alpha_w$ . Options are `"cv"`, `"oob"`, and `FALSE` standing for calibration with cross-validation, OOB calibration, and no calibration, respectively. See below for details. The default is `"cv"`.
`coverage_range`	The allowed target calibration range for coverage level. $\alpha_w$ is selected such that the `"cv"` or `"oob"` coverage is within `coverage_range`.
`numfolds`	Number of folds for calibration with cross-validation. The default is 5 folds.
`params_ranger`	List of parameters that should be passed to `ranger`. In the default parameter set, `num.trees` = 2000, `mtry` = $px/3$ (rounded up), `min.node.size` = 5, `replace` = TRUE. See `ranger` for possible parameters.
`oob`	Should out-of-bag (OOB) predictions and prediction intervals for the training observations be returned?

Value

A list with the following components:

`pred_interval`	Prediction intervals for test data. A list containing lower and upper bounds.
`test_pred`	Bias-corrected random forest predictions for test data.
`alphaw`	Working level of `alpha`, i.e. $\alpha_w$ . If `calibration = FALSE`, it returns `NULL`.
`test_response`	If available, test response.
`oob_pred_interval`	Out-of-bag (OOB) prediction intervals for train data. Prediction intervals are built with `alpha`. If `oob = FALSE`, it returns `NULL`.
`oob_pred`	Bias-corrected out-of-bag (OOB) predictions for train data. If `oob = FALSE`, it returns `NULL`.
`train_response`	Train response.

Details

Calibration process

Let ( $1-\alpha$ ) be the target coverage level. The goal of the calibration is to find the value of $\alpha_w$ , which is the working level of $\alpha$ called by Roy and Larocque (2020), such that the coverage level of the PIs for the training observations is closest to the target coverage level. Two calibration procedures are provided: calibration with cross-validation and out-of-bag (OOB) calibration.

In calibration with CV, we apply k-fold cross-validation to form prediction intervals for the training observations. In each fold, we split the original training data set into training and testing sets. For the training set, we train a one-step boosted random forest and compute the OOB residuals. Then, for each observation in the testing set, we build a PI. After completing CV, we compute the coverage level with the constructed PIs and if the coverage is not within the acceptable coverage range (coverage_range), then we apply a grid search to find the $\alpha_w$ such that $\alpha_w$ is the closest to the target $\alpha$ among the set of $\alpha_w$ 's that ensures the target coverage level for the constructed PIs. Once we find the $\alpha_w$ , we use this level to build the PI for the new observations.
The OOB calibration procedure is proposed by Roy and Larocque (2020) and it is the default calibration procedure of rfpi(). See details section of rfpi() for the detailed explanation of this calibration procedure.

In terms of computational time, OOB calibration is faster than calibration with CV. However, empirical results show that OOB calibration may result in conservative prediction intervals. Therefore, the recommended calibration procedure for the PIBF method is calibration with CV.

References

Alakus, C., Larocque, D., and Labbe, A. (2021). RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests. arXiv preprint arXiv:2106.08217.

Roy, M. H., & Larocque, D. (2020). Prediction intervals with random forests. Statistical methods in medical research, 29(1), 205-229. doi:10.1177/0962280219829885.

Examples

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

## define train/test split
testindex <- 1:10
trainindex <- sample(11:nrow(BostonHousing), size = 100, replace = FALSE)
traindata <- BostonHousing[trainindex, ]
testdata <- BostonHousing[testindex, ]
px <- ncol(BostonHousing) - 1

## construct 95% PI with "cv" calibration using 5-folds
out <- pibf(formula = medv ~ ., traindata = traindata,
  testdata = testdata, calibration = "cv", numfolds = 5,
  params_ranger = list(num.trees = 40))

## get the PI for the first observation in the testdata
c(out$pred_interval$lower[1], out$pred_interval$upper[1])

## get the bias-corrected random forest predictions for testdata
out$test_pred

## construct 90% PI with "oob" calibration
out2 <- pibf(formula = medv ~ ., traindata = traindata,
  testdata = testdata, alpha = 0.1, calibration = "oob",
  coverage_range = c(0.89,91), params_ranger = list(num.trees = 40))

## get the PI for the testdata
out2$pred_interval

## get the working level of alpha (alphaw)
out2$alphaw

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

## define train/test split
testindex <- 1:10
trainindex <- sample(11:nrow(BostonHousing), size = 100, replace = FALSE)
traindata <- BostonHousing[trainindex, ]
testdata <- BostonHousing[testindex, ]
px <- ncol(BostonHousing) - 1

## construct 95% PI with "cv" calibration using 5-folds
out <- pibf(formula = medv ~ ., traindata = traindata,
  testdata = testdata, calibration = "cv", numfolds = 5,
  params_ranger = list(num.trees = 40))

## get the PI for the first observation in the testdata
c(out$pred_interval$lower[1], out$pred_interval$upper[1])

## get the bias-corrected random forest predictions for testdata
out$test_pred

## construct 90% PI with "oob" calibration
out2 <- pibf(formula = medv ~ ., traindata = traindata,
  testdata = testdata, alpha = 0.1, calibration = "oob",
  coverage_range = c(0.89,91), params_ranger = list(num.trees = 40))

## get the PI for the testdata
out2$pred_interval

## get the working level of alpha (alphaw)
out2$alphaw

Plot constructed prediction intervals for `('rfpredinterval', 'piall')` objects

Description

Plots the 16 constructed PIs obtained with piall() for a test observation. For each method, the red point presents the point prediction and blue line shows the constructed prediction interval for the test observation. If the true response of the test observation is known, it is demonstrated with a dashed vertical line. Note that we may have multiple prediction intervals with the HDR PI method.

Usage

## S3 method for class 'rfpredinterval'
plot(x, test_id = 1, sort = TRUE, show_response = TRUE, ...)
## S3 method for class 'rfpredinterval'
plot(x, test_id = 1, sort = TRUE, show_response = TRUE, ...)

Arguments

`x`	An object of class `('rfpredinterval', 'piall')`.
`test_id`	Integer value specifying the test observation to be plotted. The default is 1.
`sort`	Should the prediction intervals be sorted according to their lengths in the plot? The default is `TRUE`.
`show_response`	Should the true response value of the test observation (if available) be displayed in the plot?
`...`	Optional arguments to be passed to other methods.

Value

Invisibly, the prediction intervals and point predictions that were plotted for the test observation.

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, ]

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

## plot the constructed PIs for test_id = 1 with all methods
plot(out, test_id = 1)



## 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, ]

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

## plot the constructed PIs for test_id = 1 with all methods
plot(out, test_id = 1)

Print summary output

Description

Print summary output from pibf(), rfpi(), or piall() functions. This is the default print method for the package.

Usage

## S3 method for class 'rfpredinterval'
print(x, ...)
## S3 method for class 'rfpredinterval'
print(x, ...)

Arguments

`x`	An object of class `('rfpredinterval', 'piall')`, `('rfpredinterval', 'pibf')`, or `('rfpredinterval', 'rfpi')`.
`...`	Optional arguments to be passed to other methods.

Examples

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

## define train/test split
testindex <- 1:10
trainindex <- sample(11:nrow(BostonHousing), size = 100, replace = FALSE)
traindata <- BostonHousing[trainindex, ]
testdata <- BostonHousing[testindex, ]
px <- ncol(BostonHousing) - 1

## construct 95% PI with "cv" calibration using 5-folds
out <- pibf(formula = medv ~ ., traindata = traindata,
  testdata = testdata, calibration =  "oob",
  params_ranger = list(num.trees = 40))

## print summary output
print(out)

## contruct 95% PI with "ls" split rule, "lm", "quant" and "spi" PI methods
## with calibration and use "ranger" package for RF training
out2 <- rfpi(formula = medv ~ ., traindata = traindata,
  testdata = testdata, split_rule = "ls", pi_method = c("lm", "quant", "spi"),
  rf_package = "ranger", params_ranger = list(num.trees = 50))

## print summary output
print(out2)

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

## define train/test split
testindex <- 1:10
trainindex <- sample(11:nrow(BostonHousing), size = 100, replace = FALSE)
traindata <- BostonHousing[trainindex, ]
testdata <- BostonHousing[testindex, ]
px <- ncol(BostonHousing) - 1

## construct 95% PI with "cv" calibration using 5-folds
out <- pibf(formula = medv ~ ., traindata = traindata,
  testdata = testdata, calibration =  "oob",
  params_ranger = list(num.trees = 40))

## print summary output
print(out)

## contruct 95% PI with "ls" split rule, "lm", "quant" and "spi" PI methods
## with calibration and use "ranger" package for RF training
out2 <- rfpi(formula = medv ~ ., traindata = traindata,
  testdata = testdata, split_rule = "ls", pi_method = c("lm", "quant", "spi"),
  rf_package = "ranger", params_ranger = list(num.trees = 50))

## print summary output
print(out2)

Prediction intervals with random forests

Description

Constructs prediction intervals with 15 distinct variations proposed by Roy and Larocque (2020). The variations include two aspects: The method used to build the forest and the method used to build the prediction interval. There are three methods to build the forest, (i) least-squares (LS), (ii) L1 and (iii) shortest prediction interval (SPI) from the CART paradigm. There are five methods for constructing prediction intervals, classical method, shortest prediction interval, quantile method, highest density region, and contiguous HDR.

Usage

rfpi(
  formula,
  traindata,
  testdata,
  alpha = 0.05,
  split_rule = c("ls", "l1", "spi"),
  pi_method = c("lm", "spi", "quant", "hdr", "chdr"),
  calibration = TRUE,
  rf_package = c("rfsrc", "ranger"),
  params_rfsrc = list(ntree = 2000, mtry = ceiling(px/3), nodesize = 5, samptype =
    "swr"),
  params_ranger = list(num.trees = 2000, mtry = ceiling(px/3), min.node.size = 5,
    replace = TRUE),
  params_calib = list(range = c(1 - alpha - 0.005, 1 - alpha + 0.005), start = (1 -
    alpha), step = 0.01, refine = TRUE),
  oob = FALSE
)
rfpi(
  formula,
  traindata,
  testdata,
  alpha = 0.05,
  split_rule = c("ls", "l1", "spi"),
  pi_method = c("lm", "spi", "quant", "hdr", "chdr"),
  calibration = TRUE,
  rf_package = c("rfsrc", "ranger"),
  params_rfsrc = list(ntree = 2000, mtry = ceiling(px/3), nodesize = 5, samptype =
    "swr"),
  params_ranger = list(num.trees = 2000, mtry = ceiling(px/3), min.node.size = 5,
    replace = TRUE),
  params_calib = list(range = c(1 - alpha - 0.005, 1 - alpha + 0.005), start = (1 -
    alpha), step = 0.01, refine = TRUE),
  oob = FALSE
)

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.
`split_rule`	Split rule for building a forest. Options are `"ls"` for CART with least-squares (LS) splitting rule, `"l1"` for CART with L1 splitting rule, `"spi"` for CART with shortest prediction interval (SPI) splitting rule. The default is `"ls"`.
`pi_method`	Methods for building a prediction interval. Options are `"lm"` for classical method, `"spi"` for shortest prediction interval, `"quant"` for quantile method, `"hdr"` for highest density region, and `"chdr"` for contiguous HDR. The default is to use all methods for PI construction. Single method or a subset of methods can be applied.
`calibration`	Apply OOB calibration for finding working level of `alpha`, i.e. $\alpha_w$ . See below for details. The default is `TRUE`.
`rf_package`	Random forest package that can be used for RF training. Options are `"rfsrc"` for `randomForestSRC` and `"ranger"` for `ranger` packages. Split rule `"ls"` can be used with both packages. However, `"l1"` and `"spi"` split rules can only be used with `"rfsrc"`. The default is `"rfsrc"`.
`params_rfsrc`	List of parameters that should be passed to `randomForestSRC`. In the default parameter set, `ntree` = 2000, `mtry` = $px/3$ (rounded up), `nodesize` = 5, `samptype` = "swr". See `randomForestSRC` for possible parameters.
`params_ranger`	List of parameters that should be passed to `ranger`. In the default parameter set, `num.trees` = 2000, `mtry` = $px/3$ (rounded up), `min.node.size` = 5, `replace` = TRUE. See `ranger` for possible parameters.
`params_calib`	List of parameters for calibration procedure. `range` is the allowed target calibration range for coverage level. The value that provides a coverage level within the range is chosen as $\alpha_w$ . `start` is the initial coverage level to start calibration procedure. `step` is the coverage step size for each calibration iteration. `refine` is the gradual decrease in `step` value when close to target coverage level, the default is `TRUE` which allows gradual decrease.
`oob`	Should out-of-bag (OOB) predictions and prediction intervals for the training observations be returned?

Value

A list with the following components:

`lm_interval`	Prediction intervals for test data with the classical method. A list containing lower and upper bounds.
`spi_interval`	Prediction intervals for test data with SPI method. A list containing lower and upper bounds.
`hdr_interval`	Prediction intervals for test data with 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.
`chdr_interval`	Prediction intervals for test data with contiguous HDR method. A list containing lower and upper bounds.
`quant_interval`	Prediction intervals for test data with quantiles method. A list containing lower and upper bounds.
`test_pred`	Random forest predictions for test data.
`test_response`	If available, test response.
`alphaw`	Working level of `alpha`, i.e. $\alpha_w$ . A numeric array for the PI methods entered with `pi_method`. If `calibration = FALSE`, it returns `NULL`.
`split_rule`	Split rule used for building the random forest.
`rf_package`	Random forest package that was used for RF training.
`oob_pred_interval`	Out-of-bag (OOB) prediction intervals for train data. Prediction intervals are built with `alpha`. If `oob = FALSE`, it returns `NULL`.
`oob_pred`	Out-of-bag (OOB) predictions for train data. If `oob = FALSE`, it returns `NULL`.
`train_response`	Train response.

Details

Calibration process

The calibration procedure uses the "Bag of Observations for Prediction" (BOP) idea. BOP for a new observation is built with the set inbag observations that are in the same terminal nodes as the new observation. The calibration procedure uses the BOPs constructed for the training observations. BOP for a training observation is built using only the trees where this training observation is out-of-bag (OOB).

Let ( $1-\alpha$ ) be the target coverage level. The goal of the calibration is to find the value of $\alpha_w$ , which is the working level of $\alpha$ called by Roy and Larocque (2020), such that the coverage level of the prediction intervals for the training observations is closest to the target coverage level. The idea is to find the value of $\alpha_w$ using the OOB-BOPs. Once found, ( $1-\alpha_w$ ) becomes the level used to build the prediction intervals for the new observations.

References

Roy, M. H., & Larocque, D. (2020). Prediction intervals with random forests. Statistical methods in medical research, 29(1), 205-229. doi:10.1177/0962280219829885.

Examples

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

## define train/test split
testindex <- 1:10
trainindex <- sample(11:nrow(BostonHousing), size = 100, replace = FALSE)
traindata <- BostonHousing[trainindex, ]
testdata <- BostonHousing[testindex, ]
px <- ncol(BostonHousing) - 1

## contruct 90% PI with "l1" split rule and "spi" PI method with calibration
out <- rfpi(formula = medv ~ ., traindata = traindata,
  testdata = testdata, alpha = 0.1, calibration = TRUE,
  split_rule = "l1", pi_method = "spi", params_rfsrc = list(ntree = 50),
  params_calib = list(range = c(0.89, 0.91), start = 0.9, step = 0.01,
  refine = TRUE))

## get the PI with "spi" method for first observation in the testdata
c(out$spi_interval$lower[1], out$spi_interval$upper[1])

## get the random forest predictions for testdata
out$test_pred

## get the working level of alpha (alphaw)
out$alphaw

## contruct 95% PI with "ls" split rule, "lm" and "quant" PI methods
## with calibration and use "ranger" package for RF training
out2 <- rfpi(formula = medv ~ ., traindata = traindata,
  testdata = testdata, split_rule = "ls", pi_method = c("lm", "quant"),
  rf_package = "ranger", params_ranger = list(num.trees = 50))

## get the PI with "quant" method for the testdata
cbind(out2$quant_interval$lower, out2$quant_interval$upper)

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

## define train/test split
testindex <- 1:10
trainindex <- sample(11:nrow(BostonHousing), size = 100, replace = FALSE)
traindata <- BostonHousing[trainindex, ]
testdata <- BostonHousing[testindex, ]
px <- ncol(BostonHousing) - 1

## contruct 90% PI with "l1" split rule and "spi" PI method with calibration
out <- rfpi(formula = medv ~ ., traindata = traindata,
  testdata = testdata, alpha = 0.1, calibration = TRUE,
  split_rule = "l1", pi_method = "spi", params_rfsrc = list(ntree = 50),
  params_calib = list(range = c(0.89, 0.91), start = 0.9, step = 0.01,
  refine = TRUE))

## get the PI with "spi" method for first observation in the testdata
c(out$spi_interval$lower[1], out$spi_interval$upper[1])

## get the random forest predictions for testdata
out$test_pred

## get the working level of alpha (alphaw)
out$alphaw

## contruct 95% PI with "ls" split rule, "lm" and "quant" PI methods
## with calibration and use "ranger" package for RF training
out2 <- rfpi(formula = medv ~ ., traindata = traindata,
  testdata = testdata, split_rule = "ls", pi_method = c("lm", "quant"),
  rf_package = "ranger", params_ranger = list(num.trees = 50))

## get the PI with "quant" method for the testdata
cbind(out2$quant_interval$lower, out2$quant_interval$upper)

Package 'RFpredInterval'

Help Index

RFpredInterval: A package for building prediction intervals with random forests and boosted forests

Description

Details

RFpredInterval functions

References

Boston housing data set

Description

Usage

Format

Examples

Prediction intervals with all methods

Description

Usage

Arguments

Value

See Also

Examples

Prediction intervals with boosted forests

Description

Usage

Arguments

Value

Details

References

See Also

Examples

Plot constructed prediction intervals for ('rfpredinterval', 'piall') objects

Description

Usage

Arguments

Value

See Also

Examples

Print summary output

Description

Usage

Arguments

See Also

Examples

Prediction intervals with random forests

Description

Usage

Arguments

Value

Details

References

See Also

Examples

Plot constructed prediction intervals for `('rfpredinterval', 'piall')` objects