% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GetProbs.R
\name{GetProbs}
\alias{GetProbs}
\title{Compute probabilistic forecasts or the corresponding observations}
\usage{
GetProbs(
  data,
  time_dim = "sdate",
  memb_dim = "member",
  indices_for_quantiles = NULL,
  prob_thresholds = c(1/3, 2/3),
  abs_thresholds = NULL,
  bin_dim_abs = "bin",
  weights = NULL,
  cross.val = FALSE,
  ncores = NULL
)
}
\arguments{
\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, 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 absolute thresholds between the probabilistic
categories. If NULL (default), the whole period is used. It is only used 
when 'prob_thresholds' is provided.}

\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{abs_thresholds}{A numeric array or vector of the absolute thresholds in 
the same units as \code{data}. If an array is provided, it should have at 
least 'bin_dim_abs' dimension. If it has more dimensions (e.g. different 
thresholds for different locations, i.e. lon and lat dimensions), they 
should match the dimensions of \code{data}, except the member dimension 
which should not be included. The default value is NULL and, in this case, 
'prob_thresholds' is used for calculating the probabilities.}

\item{bin_dim_abs}{A character string of the dimension name of 
'abs_thresholds' array in which category limits are stored. It will also be
the probabilistic category dimension name in the output. The default value
is 'bin'.}

\item{weights}{A named numerical array of the weights for 'data' with 
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 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.}
}
\value{
A numerical array of probabilities with dimensions c(bin_dim_abs, the rest
dimensions of 'data' except 'memb_dim'). 'bin' dimension has the length of 
probabilistic categories, i.e., \code{length(prob_thresholds) + 1}.
}
\description{
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. The absolute thresholds can also be
provided directly for probabilities calculation.
}
\examples{
data <- array(rnorm(2000), dim = c(ensemble = 25, sdate = 20, time = 4))
res <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble', 
                indices_for_quantiles = 4:17)

# abs_thresholds is provided
abs_thr1 <- c(-0.2, 0.3)
abs_thr2 <- array(c(-0.2, 0.3) + rnorm(40) * 0.1, dim = c(cat = 2, sdate = 20))
res1 <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble',
                 prob_thresholds = NULL, abs_thresholds = abs_thr1)
res2 <- GetProbs(data = data, time_dim = 'sdate', memb_dim = 'ensemble',
                 prob_thresholds = NULL, abs_thresholds = abs_thr2, bin_dim_abs = 'cat')

}
