From 2a67bbc0a7fa08dbd05ad9df1037f203591b2659 Mon Sep 17 00:00:00 2001
From: aho
Date: Thu, 30 Mar 2023 11:39:44 +0200
Subject: [PATCH] Update the document
---
man/GetProbs.Rd | 36 +++++++++++++++++++++---------------
1 file changed, 21 insertions(+), 15 deletions(-)
diff --git a/man/GetProbs.Rd b/man/GetProbs.Rd
index c967a2e..27fe68c 100644
--- a/man/GetProbs.Rd
+++ b/man/GetProbs.Rd
@@ -2,7 +2,7 @@
% Please edit documentation in R/GetProbs.R
\name{GetProbs}
\alias{GetProbs}
-\title{Compute forecast probability}
+\title{Compute probabilistic forecasts or the corresponding observations}
\usage{
GetProbs(
data,
@@ -16,31 +16,33 @@ GetProbs(
)
}
\arguments{
-\item{data}{A named numerical array of the forecast or observation with at
-least time dimension.}
+\item{data}{A named numerical array of the forecasts or observations with, at
+least, time dimension.}
\item{time_dim}{A character string indicating the name of the time dimension.
The default value is 'sdate'.}
\item{memb_dim}{A character string indicating the name of the member dimension
-to compute the probabilities of the forecast. The default value is 'member'.}
+to compute the probabilities of the forecast, or NULL if there is no member
+dimension (e.g., for observations, or for forecast with only one ensemble
+member). The default value is 'member'.}
\item{indices_for_quantiles}{A vector of the indices to be taken along 'time_dim'
-for computing the thresholds between the probabilistic categories. If NULL,
-the whole period is used. The default value is NULL.}
+for computing the absolute thresholds between the probabilistic categories.
+If NULL, the whole period is used. The default value is NULL.}
\item{prob_thresholds}{A numeric vector of the relative thresholds (from 0 to
1) between the categories. The default value is c(1/3, 2/3), which
corresponds to tercile equiprobable categories.}
\item{weights}{A named numerical array of the weights for 'data' with
-dimensions 'time_dim' and 'memb_dim' (if 'data' has it). The default value is
+dimensions 'time_dim' and 'memb_dim' (if 'data' has them). The default value is
NULL. The ensemble should have at least 70 members or span at least 10 time
steps and have more than 45 members if consistency between the weighted and
unweighted methodologies is desired.}
\item{cross.val}{A logical indicating whether to compute the thresholds between
-probabilistic categories in cross-validation. The default value is FALSE.}
+probabilistic categories in cross-validation mode. The default value is FALSE.}
\item{ncores}{An integer indicating the number of cores to use for parallel
computation. The default value is NULL.}
@@ -50,15 +52,19 @@ The default value is NULL, which means no dataset dimension.}
}
\value{
A numerical array of probabilities with dimensions c(bin, the rest dimensions
-of 'data' except 'memb_dim'). 'bin' dimension has the length of categories,
-i.e., \code{length(prob_thresholds) + 1}.
+of 'data' except 'memb_dim'). 'bin' dimension has the length of probabilistic
+categories, i.e., \code{length(prob_thresholds) + 1}.
}
\description{
-Compute the forecast probability based on the relative thresholds. A certain
-period can be specified to calculate the quantiles between each probabilistic
-category. If data has ensemble, all the members are used together to
-calculate the probabilities. Weights of each member and time can be provided.
-Cross-validation can be chosen when absolute threshold is calculated.
+Compute probabilistic forecasts from an ensemble based on the relative thresholds,
+or the probabilistic observations (i.e., which probabilistic category was observed).
+A reference period can be specified to calculate the absolute thresholds between
+each probabilistic category. The absolute thresholds can be computed in cross-validation
+mode. If data is an ensemble, the probabilities are calculated as the percentage of
+members that fall into each category. For observations (or forecast without member
+dimension), 1 means that the event happened, while 0 indicates that the event did
+not happen. Weighted probabilities can be computed if the weights are provided for
+each ensemble member and time step.
}
\examples{
data <- array(rnorm(2000), dim = c(ensemble = 25, sdate = 20, time = 4))
--
GitLab