Doc improvement in loo_subsample.R

R/loo_subsample.R

@@ -1,4 +1,6 @@
-#' Efficient approximate leave-one-out cross-validation (LOO) using subsampling
+#' Efficient approximate leave-one-out cross-validation (LOO) using subsampling,
+#' so that less costly and more approximate computation is made for all LOO-fold,
+#' and more costly and accurate computations are made only for m<N LOO-folds.
 #'
 #' @param x A function. The **Methods (by class)** section, below, has detailed
 #'   descriptions of how to specify the inputs.
@@ -84,15 +86,17 @@ loo_subsample <- function(x, ...) {
 #' @param estimator How should `elpd_loo`, `p_loo` and `looic` be estimated?
 #'  The default is `"diff_srs"`.
 #'  * `"diff_srs"`: uses the difference estimator with simple random sampling
-#'    (srs). `p_loo` is estimated using standard srs.
-#'  * `"hh"`: uses the Hansen-Hurwitz estimator with sampling proportional to
-#'    size, where `abs` of loo_approximation is used as size.
+#'    without replacement (srs). `p_loo` is estimated using standard srs.
+#'    (Magnusson et al., 2020)
+#'  * `"hh"`: uses the Hansen-Hurwitz estimator with sampling with replacement
+#'    proportional to size, where `abs` of loo_approximation is used as size.
+#'    (Magnusson et al., 2019)
 #'  * `"srs"`: uses simple random sampling and ordinary estimation.
 #'
 #' @param llgrad The gradient of the log-likelihood. This
 #'   is only used when `loo_approximation` is `"waic_grad"`,
 #'   `"waic_grad_marginal"`, or `"waic_hess"`. The default is `NULL`.
-#' @param llhess The hessian of the log-likelihood. This is only used
+#' @param llhess The Hessian of the log-likelihood. This is only used
 #'        with `loo_approximation = "waic_hess"`. The default is `NULL`.
 #'
 loo_subsample.function <-
@@ -814,7 +818,7 @@ pps_sample <- function(m, pis) {
 
 ## Constructor ---
 
-#' Construct a `psis_loo_ss} object
+#' Construct a `psis_loo_ss` object
 #'
 #' @noRd
 #' @param x A `psis_loo` object.
@@ -1052,7 +1056,7 @@ update_m_i_in_pointwise <- function(pointwise, idxs, type = "replace") {
 
 ## Estimation ---
 
-#' Estimate the elpd using the Hansen-Hurwitz estimator
+#' Estimate the elpd using the Hansen-Hurwitz estimator (Magnusson et al., 2019)
 #' @noRd
 #' @param x A `psis_loo_ss` object.
 #' @return A `psis_loo_ss` object.
@@ -1085,7 +1089,7 @@ loo_subsample_estimation_hh <- function(x) {
   update_psis_loo_ss_estimates(x)
 }
 
-#' Update a `psis_loo_ss} object with generic estimates
+#' Update a `psis_loo_ss` object with generic estimates
 #'
 #' @noRd
 #' @details
@@ -1110,7 +1114,7 @@ update_psis_loo_ss_estimates <- function(x) {
   x
 }
 
-#' Weighted Hansen-Hurwitz estimator
+#' Weighted Hansen-Hurwitz estimator (Magnusson et al., 2019)
 #' @noRd
 #' @param z Normalized probabilities for the observation.
 #' @param m_i The number of times obs i was selected.
@@ -1133,7 +1137,7 @@ whhest <- function(z, m_i, y, N) {
 }
 
 
-#' Estimate elpd using the difference estimator and srs wor
+#' Estimate elpd using the difference estimator and SRS-WOR (Magnusson et al., 2020)
 #' @noRd
 #' @param x A `psis_loo_ss` object.
 #' @return A `psis_loo_ss` object.
@@ -1153,7 +1157,7 @@ loo_subsample_estimation_diff_srs <- function(x) {
   update_psis_loo_ss_estimates(x)
 }
 
-#' Difference estimation using SRS-WOR sampling
+#' Difference estimation using SRS-WOR sampling (Magnusson et al., 2020)
 #' @noRd
 #' @param y_approx Approximated values of all observations.
 #' @param y The values observed.
@@ -1175,8 +1179,14 @@ srs_diff_est <- function(y_approx, y, y_idx) {
   t_hat_epsilon <- N * mean(y^2 - y_approx_m^2)
 
   est_list <- list(m = length(y), N = N)
+  # eq (7)
   est_list$y_hat <- t_pi_tilde + t_e
+  # eq (8)
   est_list$v_y_hat <- N^2 * (1 - m / N) * var(e_i) / m
+  # eq (9) first row second `+` should be `-`
+  # Supplementary material eq (6) has this correct
+  # Here the variance is for sum, while in the paper the variance is for mean
+  # which explains the proporional difference of 1/n
   est_list$hat_v_y <- (t_pi2_tilde + t_hat_epsilon) - # a (has been checked)
     (1/N) * (t_e^2 - est_list$v_y_hat + 2 * t_pi_tilde * est_list$y_hat - t_pi_tilde^2) # b
   est_list