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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/rarx.R
+\name{rarx}
+\alias{rarx}
+\title{Estimate parameters of ARX recursively}
+\usage{
+rarx(x, order = c(1, 1, 1), lambda = 0.95)
+}
+\arguments{
+\item{x}{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 + 1) and 
+the input-output delay}
+
+\item{lambda}{Forgetting factor(Default=\code{0.95})}
+}
+\value{
+A list containing the following objects
+\describe{
+ \item{theta}{Estimated parameters of the model. The \eqn{k^{th}} 
+ row contains the parameters associated with the \eqn{k^{th}} 
+ sample. Each row in \code{theta} has the following format: \cr
+ theta[i,:]=[a1,a2,...,ana,b1,...bnb]
+ }
+ \item{yhat}{Predicted value of the output, according to the 
+ current model - parameters based on all past data}
+}
+}
+\description{
+Estimates the parameters of a single-output ARX model of the 
+specified order from data using the recursive weighted least-squares
+algorithm.
+}
+\examples{
+Gp1 <- idpoly(c(1,-0.9,0.2),2,ioDelay=2,noiseVar = 0.1)
+Gp2 <- idpoly(c(1,-1.2,0.35),2.5,ioDelay=2,noiseVar = 0.1)
+uk = idinput(2044,'prbs',c(0,1/4)); N = length(uk);
+N1 = round(0.35*N); N2 = round(0.4*N); N3 = N-N1-N2;
+yk1 <- sim(Gp1,uk[1:N1],addNoise = T)
+yk2 <- sim(Gp2,uk[N1+1:N2],addNoise = T)
+yk3 <- sim(Gp1,uk[N1+N2+1:N3],addNoise = T)
+yk <- c(yk1,yk2,yk3)
+z <- idframe(yk,uk,1)
+g(theta,yhat) \%=\% rarx(z,c(2,1,2))
+
+}
+\references{
+Arun K. Tangirala (2015), \emph{Principles of System Identification: 
+Theory and Practice}, CRC Press, Boca Raton. Section 25.1.3
+
+Lennart Ljung (1999), \emph{System Identification: Theory for the User}, 
+2nd Edition, Prentice Hall, New York. Section 11.2
+}
+