Merge pull request #195 from StochasticTree/r-code-format

Format R code using air

Author: andrewherren

R/bart.R

@@ -1,5 +1,5 @@
 #' Calibrate the scale parameter on an inverse gamma prior for the global error variance as in Chipman et al (2022)
-#' 
+#'
 #' 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
 #'
 #' @param y Outcome to be modeled using BART, BCF or another nonparametric ensemble method.
@@ -10,7 +10,7 @@
 #' @param standardize (Optional) Whether or not outcome should be standardized (`(y-mean(y))/sd(y)`) before calibration of `lambda`. Default: `TRUE`.
 #'
 #' @return Value of `lambda` which determines the scale parameter of the global error variance prior (`sigma^2 ~ IG(nu,nu*lambda)`)
-#' @export 
+#' @export
 #'
 #' @examples
 #' n <- 100
@@ -21,14 +21,26 @@
 #' lambda <- calibrateInverseGammaErrorVariance(y, X, nu = nu)
 #' sigma2hat <- mean(resid(lm(y~X))^2)
 #' mean(var(y)/rgamma(100000, nu, rate = nu*lambda) < sigma2hat)
-calibrateInverseGammaErrorVariance <- function(y, X, W = NULL, nu = 3, quant = 0.9, standardize = TRUE) {
+calibrateInverseGammaErrorVariance <- function(
+    y,
+    X,
+    W = NULL,
+    nu = 3,
+    quant = 0.9,
+    standardize = TRUE
+) {
     # Compute regression basis
-    if (!is.null(W)) basis <- cbind(X, W)
-    else basis <- X
+    if (!is.null(W)) {
+        basis <- cbind(X, W)
+    } else {
+        basis <- X
+    }
     # Standardize outcome if requested
-    if (standardize) y <- (y-mean(y))/sd(y)
+    if (standardize) {
+        y <- (y - mean(y)) / sd(y)
+    }
     # Compute the "regression-based" overestimate of sigma^2
-    sigma2hat <- mean(resid(lm(y~basis))^2)
+    sigma2hat <- mean(resid(lm(y ~ basis))^2)
     # Calibrate lambda based on the implied quantile of sigma2hat
-    return((sigma2hat*qgamma(1-quant,nu))/nu)
+    return((sigma2hat * qgamma(1 - quant, nu)) / nu)
 }