\name{PredictInterval}
\alias{PredictInterval}
\title{Prediction skill versus forecast interval}
\description{
\code{\link{PredictInterval}} uses \code{\link{Simplex}} to evaluate
prediction accuracy as a function of forecast interval Tp.
}
\usage{
PredictInterval(dataFrame = NULL, columns, target, lib, pred, maxTp = 10, E = NULL,
                tau = -1, exclusionRadius = 0, embedded = FALSE,
                validLib = logical(0), noTime = FALSE, ignoreNan = TRUE,
                numProcess = 4, backend = "RANN", pathIn = "./",
                dataFile = "", pathOut = "./", predictFile = "",
                parameterList = FALSE, showPlot = FALSE)
}
\arguments{
\item{dataFrame}{A data.frame of input data. The first column must be a time
index or time values unless \code{noTime = TRUE}. The columns must be named.}
\item{columns}{Column name(s) to build the embedding: character vector or space-separated string.}
\item{target}{Target column name to predict.}
\item{lib}{Library (training) index range as (start end) pairs.}
\item{pred}{Prediction index range as (start end) pairs.}
\item{maxTp}{Largest forecast interval to evaluate.}
\item{E}{Embedding dimension. Required (no default); must be a positive integer unless \code{embedded = TRUE}, in which case it is inferred as the number of \code{columns}.}
\item{tau}{Embedding delay (negative selects past lags).}
\item{exclusionRadius}{Temporal (Theiler) exclusion radius around each prediction point.}
\item{embedded}{If \code{TRUE}, \code{columns} already form the embedding.}
\item{validLib}{Logical vector marking admissible library rows (or length 0 for all).}
\item{noTime}{If \code{TRUE}, synthesise a 1..N time index instead of using column 1.}
\item{ignoreNan}{Remove rows with NaN in the embedding from the library and prediction sets.}
\item{numProcess}{Number of worker processes for the parameter sweep or task grid.}
\item{backend}{Nearest-neighbour backend: \code{"RANN"} (default) or \code{"brute"}.}
\item{pathIn}{File path for input \code{dataFile}.}
\item{dataFile}{Input dataFile, .csv format.}
\item{pathOut}{Output file path for \code{predictFile}}
\item{predictFile}{Output file name, .csv format.}
\item{parameterList}{Append named list of parameters/values to return.}
\item{showPlot}{If \code{TRUE}, draw a base-graphics plot of the result.}
}
\value{A data.frame with columns \code{Tp, rho}.}
\examples{
data(TentMap)
Tp.rho = PredictInterval( dataFrame = TentMap, lib = "1 100",
pred = "201 500", E = 2, columns = "TentMap", target = "TentMap",
showPlot = FALSE )
}

\keyword{nonlinear}
