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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ACC.R
\name{ACC}
\alias{ACC}
\title{Compute the anomaly correlation coefficient between the forecast and corresponding observation}
\usage{
ACC(exp, obs, dat_dim = "dataset", space_dim = c("lat", "lon"),
avg_dim = "sdate", memb_dim = "member", lat = NULL, lon = NULL,
lonlatbox = NULL, conf = TRUE, conftype = "parametric",
conf.lev = 0.95, pval = TRUE, ncores = NULL)
}
\arguments{
\item{exp}{A numeric array of experimental anomalies with named dimensions.
It must have at least 'dat_dim' and 'space_dim'.}
\item{obs}{A numeric array of observational anomalies with named dimensions.
It must have the same dimensions as 'exp' except the length of 'dat_dim'
and 'memb_dim'.}
\item{dat_dim}{A character string indicating the name of dataset (nobs/nexp)
dimension. The default value is 'dataset'.}
\item{space_dim}{A character string vector of 2 indicating the name of the
latitude and longitude dimensions (in order) along which ACC is computed.
The default value is c('lat', 'lon').}
\item{avg_dim}{A character string indicating the name of the dimension to be
averaged. It must be one of 'time_dim'. The mean ACC is calculated along
averaged. If no need to calculate mean ACC, set as NULL. The default value
is 'sdate'.}
\item{memb_dim}{A character string indicating the name of the member
dimension. If the data are not ensemble ones, set as NULL. The default
value is 'member'.}
\item{lat}{A vector of the latitudes of the exp/obs grids. Only required when
the domain of interested is specified. The default value is NULL.}
\item{lon}{A vector of the longitudes of the exp/obs grids. Only required when
the domain of interested is specified. The default value is NULL.}
\item{lonlatbox}{A numeric vector of 4 indicating the corners of the domain of
interested: c(lonmin, lonmax, latmin, latmax). Only required when the domain
of interested is specified. The default value is NULL.}
\item{conf}{A logical value indicating whether to retrieve the confidence
intervals or not. The default value is TRUE.}
\item{conftype}{A charater string of "parametric" or "bootstrap".
"parametric" provides a confidence interval for the ACC computed by a
Fisher transformation and a significance level for the ACC from a one-sided
student-T distribution. "bootstrap" provides a confidence interval for the
ACC and MACC computed from bootstrapping on the members with 100 drawings
with replacement. To guarantee the statistical robustness of the result,
make sure that your experiment and observation always have the same number
of members. "bootstrap" requires 'memb_dim' has value. The default value is
'parametric'.}
\item{conf.lev}{A numeric indicating the confidence level for the
regression computation. The default value is 0.95.}
\item{pval}{A logical value indicating whether to compute the p-value or not.
The default value is TRUE.}
\item{ncores}{An integer indicating the number of cores to use for parallel
computation. The default value is NULL.}
}
\value{
A list containing the numeric arrays:\cr
The ACC with the dimensions c(nexp, nobs, the rest of the dimension except
space_dim and memb_dim). nexp is the number of experiment (i.e., dat_dim in
exp), and nobs is the number of observation (i.e., dat_dim in obs).
\item{conf.lower (if conftype = "parametric") or acc_conf.lower (if
conftype = "bootstrap")}{
The lower confidence interval of ACC with the same dimensions as ACC. Only
present if \code{conf = TRUE}.
\item{conf.upper (if conftype = "parametric") or acc_conf.upper (if
conftype = "bootstrap")}{
The upper confidence interval of ACC with the same dimensions as ACC. Only
present if \code{conf = TRUE}.
}
\item{p.val}{
The p-value with the same dimensions as ACC. Only present if
The mean anomaly correlation coefficient with dimensions
c(nexp, nobs, the rest of the dimension except space_dim, memb_dim, and
avg_dim). Only present if 'avg_dim' is not NULL.
}
\item{macc_conf.lower}{
The lower confidence interval of MACC with the same dimensions as MACC.
Only present if \code{conftype = "bootstrap"}.
}
\item{macc_conf.upper}{
The upper confidence interval of MACC with the same dimensions as MACC.
Only present if \code{conftype = "bootstrap"}.
}
}
\description{
Calculate the anomaly correlation coefficient for the ensemble mean of each
model and the corresponding references over a spatial domain. It can return a
forecast time series if the data contain forest time dimension, and also the
start date mean if the data contain start date dimension.
The domain of interest can be specified by providing the list
of longitudes/latitudes (lon/lat) of the data together with the corners
of the domain: lonlatbox = c(lonmin, lonmax, latmin, latmax).
}
\examples{
\dontshow{
startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
sampleData <- s2dv:::.LoadSampleData('tos', c('experiment'),
c('observation'), startDates,
leadtimemin = 1,
leadtimemax = 4,
output = 'lonlat',
latmin = 27, latmax = 48,
lonmin = -12, lonmax = 40)
}
sampleData$mod <- Season(sampleData$mod, monini = 11, moninf = 12, monsup = 2)
sampleData$obs <- Season(sampleData$obs, monini = 11, moninf = 12, monsup = 2)
clim <- Clim(sampleData$mod, sampleData$obs)
ano_exp <- Ano(sampleData$mod, clim$clim_exp)
ano_obs <- Ano(sampleData$obs, clim$clim_obs)
acc_bootstrap <- ACC(ano_exp, ano_obs, conftype = 'bootstrap')
res <- array(c(acc$conf.lower, acc$acc, acc$conf.upper, acc$p.val),
dim = c(dim(acc$acc), 4))
res_bootstrap <- array(c(acc$acc_conf.lower, acc$acc, acc$acc_conf.upper, acc$p.val),
dim = c(dim(acc$acc), 4))