% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/estpoly.R
\name{estARX}
\alias{estARX}
\title{Estimate ARX Models}
\usage{
estARX(data, order = c(0, 1, 0))
}
\arguments{
\item{data}{an object of class \code{idframe}}

\item{order:}{Specification of the orders: the three integer components
(na,nb,nk) are the order of polynolnomial A, order of polynomial B and
the input-output delay}
}
\value{
An object with classes \code{estARX} and \code{estPoly}, containing
the following elements:

\tabular{ll}{
   \code{coefficients} \tab an \code{idpoly} object containing the
   fitted coefficients \cr
   \code{vcov} \tab the covariance matrix of the fitted coefficients\cr
   \code{sigma} \tab the standard deviation of the innovations\cr
   \code{df} the residual degrees of freedom\tab \cr
   \code{fitted.values} \tab the predicted response
   \code{residuals} \tab the residuals  \cr
   \code{call} \tab the matched call \cr
   \code{time} \tab the time of the data used \cr
 }
}
\description{
Fit an ARX model of the specified order given the input-output data
}
\details{
ARX models are of the form \\
}
\examples{
data(arxsim)
model <- estARX(data,c(2,1,1))
summary(model) # obtain estimates and their covariances
plot(model) # plot the predicted and actual responses
}
\references{
Arun K. Tangirala (2015), Principles of System Identification: Theory and
Practice, CRC Press, Boca Raton. Section 21.6.1

Lennart Ljung (1999) System Identification: Theory for the User,
2nd Edition, Prentice Hall, New York. Section 10.1
}