pinterval_bcbootstrap {pintervals} | R Documentation |
Bin-conditional bootstrap prediction intervals
Description
This function computes bootstrapped prediction intervals with a confidence level of 1-alpha for a vector of (continuous) predicted values using bin-conditional bootstrapped prediction errors. The prediction errors to bootstrap from are computed using either a calibration set with predicted and true values or a set of pre-computed prediction errors from a calibration dataset or other data which the model was not trained on (e.g. OOB errors from a model using bagging). The function returns a tibble containing the predicted values along with the lower and upper bounds of the prediction intervals.
Currently not working as intended. May be removed in future versions.
Usage
pinterval_bcbootstrap(
pred,
calib,
calib_truth = NULL,
calib_bins = NULL,
breaks = NULL,
nbins = NULL,
calib_bin_type = c("prediction", "truth"),
error_type = c("raw", "absolute"),
alpha = 0.1,
n_bootstraps = 1000,
lower_bound = NULL,
upper_bound = NULL,
right = TRUE
)
Arguments
pred |
Vector of predicted values |
calib |
A numeric vector of predicted values in the calibration partition or a 2 column tibble or matrix with the first column being the predicted values and the second column being the truth values |
calib_truth |
A numeric vector of true values in the calibration partition. Only required if calib is a numeric vector |
calib_bins |
A vector of bin identifiers for the calibration set |
breaks |
A vector of break points for the bins to manually define the bins. If NULL, lower and upper bounds of the bins are calculated as the minimum and maximum values of each bin in the calibration set. Must be provided if calib_bins or nbins are not provided, either as a vector or as the last column of a calib tibble. |
nbins |
Automatically chop the calibration set into nbins based on the true values with approximately equal number of observations in each bin. Must be provided if calib_bins or breaks are not provided. |
calib_bin_type |
A string specicying whether the bins are based on the predicted values ('prediction') or the true values ('truth'). Default is 'prediction'. Ignored if calib_bins is provided. |
error_type |
The type of error to use for the prediction intervals. Can be 'raw' or 'absolute'. If 'raw', bootstrapping will be done on the raw prediction errors. If 'absolute', bootstrapping will be done on the absolute prediction errors with random signs. Default is 'raw' |
alpha |
The confidence level for the prediction intervals. Must be a single numeric value between 0 and 1 |
n_bootstraps |
The number of bootstraps to perform. Default is 1000 |
lower_bound |
Optional minimum value for the prediction intervals. If not provided, the minimum (true) value of the calibration partition will be used |
upper_bound |
Optional maximum value for the prediction intervals. If not provided, the maximum (true) value of the calibration partition will be used |
right |
Parameter passed to cut function to determine which side of the bin interval is closed. Default is TRUE |