An R package for constructing frequentist prediction regions using indirect information.
Bersson and Hoff (2023). Frequentist Prediction Sets for Species Abundance using Indirect Information.
Bersson and Hoff (2022). Optimal Conformal Prediction for Small Areas.
install_packages("fabPrediction")
library(fabPrediction)To load the package:
library(fabPrediction) The two main functions are - predictionInterval, which
constructs prediction intervals for a continuous response. This function
can be used to construct nonparametric FAB or distance-to-average
conformal intervals, or parametric normal or Bayesian intervals. -
predictionSet, which constructs prediction sets for a
categorical counts response. This function can be used to construct
nonparametric FAB or direct sets, or a parametric Bayesian set.
Construction of basic FAB prediction regions are demonstrated below. Please see the vignette for full package capabilities, including empirical Bayes procedures to obtain estimates of prior hyperparameters based on auxiliary data.
We wlil demonstrate usage on a random normal sample of length 10.
y = rnorm(10)A FAB prediction interval with 1-alpha coverage can be
constructed for these data based on a prior parameters mu
and tau2 from a Normal-Normal working model:
y_PI = predictionInterval(y, method = "FAB",
alpha = .05,
mu = 0, tau2 = 1/2)and plotted:
plot(y_PI)We wlil demonstrate usage on a random multinomial sample for 10
categories based on a heterogeneous prior concentration
gamma.
gamma = c(10:1)
y = c(rmultinom(1,20,rdirichlet(gamma)))A FAB prediction set with 1-alpha coverage can be
constructed for these data based on an estimate of the prior parameter
gamma from a Multinomial-Dirichlet working model:
y_PS = predictionSet(y, method = "FAB",
alpha = .15,
gamma = gamma)And this prediction set can be plotted:
plot(y_PS)