Add rejection sampling using PMF Lognormal

NAMESPACE

@@ -6,7 +6,9 @@ export(flow_frequency_sampler)
 export(flow_frequency_sampler_expected)
 export(hydrograph_setup)
 export(mod_puls_routing)
+export(pmf_stage_lognormal)
 export(qp3)
+export(rejection_sampling_stage)
 export(rfa_simulate)
 export(scale_hydrograph)
 export(stage2aep)
@@ -16,7 +18,9 @@ importFrom(stats,approx)
 importFrom(stats,approxfun)
 importFrom(stats,pnorm)
 importFrom(stats,qgamma)
+importFrom(stats,qlnorm)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)
+importFrom(stats,rlnorm)
 importFrom(stats,runif)
 importFrom(utils,write.csv)

R/imports.R

@@ -1,3 +1,3 @@
-#' @importFrom stats approx approxfun pnorm qgamma qnorm quantile runif
+#' @importFrom stats approx approxfun pnorm qgamma qnorm quantile runif qlnorm rlnorm
 #' @importFrom utils write.csv
 NULL

R/rejection_sampling.R

@@ -0,0 +1,201 @@
+#' Thin rejection sampling output for plotting
+#'
+#' Thins a large sample of sorted z-scores and stages for plotting efficiency.
+#' Retains 5,000 evenly-spaced points across the full range plus the last
+#' 10,000 points for dense tail coverage.
+#'
+#' @param n            Integer. Total number of samples.
+#' @param z_sorted     Numeric vector of sorted z-scores (ascending), length \code{n}.
+#' @param stage_sorted Numeric vector of sorted stage values (ascending), length \code{n}.
+#'
+#' @return A data frame with columns \code{z_aep} and \code{stage}.
+#'
+#' @keywords internal
+thin_samples <- function(n, z_sorted, stage_sorted) {
+  thin_idx <- unique(c(
+    round(seq(1, n, length.out = 5000)),
+    (n - 10000):n
+  ))
+  thin_idx <- sort(unique(thin_idx))
+  thin_idx <- thin_idx[thin_idx >= 1 & thin_idx <= n]
+  data.frame(
+    z_aep = z_sorted[thin_idx],
+    stage = stage_sorted[thin_idx]
+  )
+}
+
+#' Parameterize a Three-Parameter Lognormal PMF for Stage
+#'
+#' Computes the parameters of a three-parameter lognormal distribution for use
+#' as a probabilistic maximum stage (PMF) in rejection sampling. The shift
+#' parameter defines the lower bound of the distribution.
+#'
+#' @param pmf_shift Numeric. Lower bound (shift) of the lognormal distribution.
+#'   Must be greater than \code{pmf_mean}.
+#' @param pmf_mean Numeric. Mean of the PMF stage distribution. Must be less
+#'   than \code{pmf_shift}.
+#' @param pmf_sigma Numeric. Standard deviation on the log scale. Defaults to
+#'   \code{0.5}.
+#'
+#' @return A named list with the following elements:
+#' \describe{
+#'   \item{pmf_shift}{Lower bound of the distribution.}
+#'   \item{pmf_mean}{Mean of the distribution.}
+#'   \item{pmf_sigma}{Standard deviation on the log scale.}
+#'   \item{pmf_mu}{Derived location parameter on the log scale.}
+#'   \item{pmf_p05}{5th percentile of the PMF stage distribution.}
+#'   \item{pmf_p95}{95th percentile of the PMF stage distribution.}
+#' }
+#'
+#' @examples
+#' pmf <- pmf_stage_lognormal(pmf_shift = 1200, pmf_mean = 1150, pmf_sigma = 0.5)
+#' pmf$pmf_p05
+#' pmf$pmf_p95
+#'
+#' @export
+pmf_stage_lognormal <- function(pmf_shift, pmf_mean, pmf_sigma = 0.5){
+  if (pmf_mean >= pmf_shift) {
+    cli::cli_abort(c(
+      "{.arg pmf_mean} must be less than {.arg pmf_shift}.",
+      "x" = "{.arg pmf_mean} is {.val {pmf_mean}} but {.arg pmf_shift} is {.val {pmf_shift}}."
+    ))
+  }
+
+  # Derive mu ====
+  pmf_mu    <- log(pmf_mean - pmf_shift) - (pmf_sigma^2 / 2)
+
+  # Summary stats
+  # pmf_median <- pmf_shift + exp(pmf_mu)
+  # pmf_sd     <- sqrt((exp(pmf_sigma^2) - 1) * exp(2 * pmf_mu + pmf_sigma^2))
+  pmf_p05    <- pmf_shift + qlnorm(0.05, pmf_mu, pmf_sigma)
+  pmf_p95    <- pmf_shift + qlnorm(0.95, pmf_mu, pmf_sigma)
+
+  # create pmf_stage_LN as a list
+  pmf_stage_LN <- list(pmf_shift = pmf_shift,
+                       pmf_mean = pmf_mean,
+                       pmf_sigma = pmf_sigma,
+                       pmf_mu = pmf_mu,
+                       pmf_p05 = pmf_p05,
+                       pmf_p95 = pmf_p95)
+
+  return(pmf_stage_LN)
+}
+
+#' Rejection Sampling of Stage Bounded by a Probabilistic Maximum Stage
+#'
+#' Draws \code{n_samples} stage values from a stage-frequency curve, rejecting
+#' any draws that exceed a probabilistic maximum stage (PMF) sampled from a
+#' three-parameter lognormal distribution. Accepted samples are ranked and
+#' assigned Weibull plotting positions for use in frequency analysis.
+#'
+#' @param pmf_stage_LN A named list returned by \code{\link{pmf_stage_lognormal}}
+#'   containing the lognormal PMF parameters.
+#' @param stage_freq_df A data frame with AEP in column 1 and stage in column 2.
+#'   Mutually exclusive with \code{aep} and \code{stage}.
+#' @param aep Numeric vector of annual exceedance probabilities. Must be
+#'   supplied with \code{stage}. Mutually exclusive with \code{stage_freq_df}.
+#' @param stage Numeric vector of stages corresponding to \code{aep}. Must be
+#'   supplied with \code{aep}. Mutually exclusive with \code{stage_freq_df}.
+#' @param n_samples Integer. Number of samples to draw. Defaults to \code{1e7}.
+#'
+#' @return A data frame of thinned accepted samples with columns for z-score
+#'   and stage, suitable for plotting a probabilistically bounded stage-frequency
+#'   curve. Output is produced by \code{thin_samples()}.
+#'
+#' @seealso \code{\link{pmf_stage_lognormal}}
+#'
+#' @examples
+#' \dontrun{
+#' pmf <- pmf_stage_lognormal(pmf_shift = 1200, pmf_mean = 1150, pmf_sigma = 0.5)
+#'
+#' result <- rejection_sampling_stage(
+#'   pmf_stage_LN  = pmf,
+#'   stage_freq_df = my_stage_freq_df,
+#'   n_samples     = 1e7
+#' )
+#' }
+#'
+#' @export
+rejection_sampling_stage <- function(pmf_stage_LN, stage_freq_df = NULL, aep = NULL, stage = NULL, n_samples = 1E7){
+  # Resolve inputs
+  if (!is.null(stage_freq_df)) {
+    if (!is.null(aep) || !is.null(stage)) {
+      cli::cli_abort("Provide either {.arg stage_freq_df} or {.arg aep}/{.arg stage}, not both.")
+    }
+    aep   <- stage_freq_df[[1]]
+    stage <- stage_freq_df[[2]]
+
+  } else if (!is.null(aep) && !is.null(stage)) {
+    # use as-is
+
+  } else {
+    cli::cli_abort(c(
+      "Must provide either {.arg stage_freq_df} or both {.arg aep} and {.arg stage}.",
+      "i" = "Supply a data frame via {.arg stage_freq_df}, or pass numeric vectors to {.arg aep} and {.arg stage} directly."
+    ))
+  }
+
+  # PMF Log-Normal Parameters ==================================================
+  pmf_shift <- pmf_stage_LN[["pmf_shift"]]
+  pmf_mu    <- pmf_stage_LN[["pmf_mu"]]
+  pmf_sigma <- pmf_stage_LN[["pmf_sigma"]]
+
+  # Transform to z-space for interpolation function ============================
+  z_aep <- qnorm(1-aep)
+  zaep_stage <- approxfun(z_aep, stage, rule = 2)
+
+  # First sample rejection =====================================================
+  # Sample AEPs
+  aep_draws <- runif(n_samples, min = 1e-9, max = 1 - 1e-9)
+
+  # Return Stage corresponding to sampled AEP (as z-value)
+  stage_draws <- zaep_stage(qnorm(1 - aep_draws))
+
+  # Randomly Sample PMF Stage from log-normal above
+  pmf_draws <- pmf_shift + rlnorm(n_samples, pmf_mu, pmf_sigma)
+
+  # Reject (aep,stage) where the stage exceeds sampled PMF Stage
+  reject_idx <- which(stage_draws > pmf_draws)
+
+  # Rejection Iterations =======================================================
+  iter <- 0
+  while (length(reject_idx) > 0) {
+    # Number of rejects
+    n_rej <- length(reject_idx)
+    # New AEP sample
+    aep_new <- runif(n_rej, min = 1e-9, max = 1 - 1e-9)
+    # Corresponding stage sample
+    stage_new <- zaep_stage(qnorm(1 - aep_new))
+    # New PMF Stage sample
+    pmf_new <- pmf_shift + rlnorm(n_rej, pmf_mu, pmf_sigma)
+    # Replace rejections with resampled aep,stage
+    stage_draws[reject_idx] <- stage_new
+    pmf_draws[reject_idx] <- pmf_new
+    # Next rejection index
+    reject_idx <- reject_idx[stage_new > pmf_new]
+    # Count iterations
+    iter <- iter + 1
+    if (iter >= 1E3) {
+      cli::cli_warn(c(
+        "Rejection sampling did not converge after {iter} iterations.",
+        "i" = "{length(reject_idx)} sample{?s} remain unresolved.",
+        "i" = "Check that {.arg pmf_stage_LN} is consistent with the provided stage-frequency curve."
+      ))
+      break
+    }
+  }
+
+  # Assign Weibull to the accepted samples =====================================
+  # Sort Stages
+  stage_sorted <- sort(stage_draws)
+
+  # Assign Weibull pp
+  weibull_aep <- 1 - seq_len(n_samples) / (n_samples + 1)
+
+  # Convert to z_aep
+  z_sorted <- qnorm(1 - weibull_aep)
+
+  # Thin for plotting purposes
+  probabilistic_bounded <- thin_samples(n_samples, z_sorted, stage_sorted)
+  return(probabilistic_bounded)
+}

R/utils.R

@@ -8,26 +8,3 @@ power_function <- function(x) {
    10^(1:x)
 }
 
-#' Thin samples
-#' Thin a large sample of sorted z-scores and stages for efficiency.
-#' Retains 5,000 evenly-spaced points across the full range plus the last
-#' 10,000 points for dense coverage at the extreme tail.
-#'
-#' @param n            Total number of samples
-#' @param z_sorted     Sorted z-scores (ascending), length n
-#' @param stage_sorted Sorted stage values (ascending), length n
-#' @return A tibble with columns z_aep and stage
-thin_samples <- function(n, z_sorted, stage_sorted) {
-
-  thin_idx <- unique(c(
-    round(seq(1, n, length.out = 5000)),
-    (n - 10000):n
-  ))
-  thin_idx <- sort(unique(thin_idx))
-  thin_idx <- thin_idx[thin_idx >= 1 & thin_idx <= n]
-
-  data.frame(
-    z_aep = z_sorted[thin_idx],
-    stage = stage_sorted[thin_idx]
-  )
-}

_pkgdown.yml

@@ -40,6 +40,8 @@ reference:
   - interpolate_stage_matrix
   - power_function
   - theme_rfar_conceptual
+  - pmf_stage_lognormal
+  - rejection_sampling_stage
 - title: "Datasets"
   desc: "Example datasets included with rfaR"
   contents: