Calibrate the scale parameter on an inverse gamma prior for the global error variance as in Chipman et al (2022) — calibrate_inverse_gamma_error

Chipman, H., George, E., Hahn, R., McCulloch, R., Pratola, M. and Sparapani, R. (2022). Bayesian Additive Regression Trees, Computational Approaches. In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels). https://doi.org/10.1002/9781118445112.stat08288

Usage

calibrate_inverse_gamma_error_variance(
  y,
  X,
  W = NULL,
  nu = 3,
  quant = 0.9,
  standardize = TRUE
)

Arguments

y: Outcome to be modeled using BART, BCF or another nonparametric ensemble method.
X: Covariates to be used to partition trees in an ensemble or series of ensemble.
W: (Optional) Basis used to define a "leaf regression" model for each decision tree. The "classic" BART model assumes a constant leaf parameter, which is equivalent to a "leaf regression" on a basis of all ones, though it is not necessary to pass a vector of ones, here or to the BART function. Default: NULL.
nu: The shape parameter for the global error variance's IG prior. The scale parameter in the Sparapani et al (2021) parameterization is defined as nu*lambda where lambda is the output of this function. Default: 3.
quant: (Optional) Quantile of the inverse gamma prior distribution represented by a linear-regression-based overestimate of sigma^2. Default: 0.9.
standardize: (Optional) Whether or not outcome should be standardized ((y-mean(y))/sd(y)) before calibration of lambda. Default: TRUE.

Value

Value of lambda which determines the scale parameter of the global error variance prior (sigma^2 ~ IG(nu,nu*lambda))

Examples

n <- 100
p <- 5
X <- matrix(runif(n*p), ncol = p)
y <- 10*X[,1] - 20*X[,2] + rnorm(n)
nu <- 3
lambda <- calibrate_inverse_gamma_error_variance(y, X, nu = nu)
sigma2hat <- mean(resid(lm(y~X))^2)
mean(var(y)/rgamma(100000, nu, rate = nu*lambda) < sigma2hat)
#> [1] 0.90021