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author | Suraj Yerramilli | 2016-02-17 11:29:05 +0530 |
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committer | Suraj Yerramilli | 2016-02-17 11:29:05 +0530 |
commit | 9a5d12a60d6c0e6dd415e97116a4aaf91c80dd0a (patch) | |
tree | 98fcd2a786e3c6d1ad9f0e4a280dcff0c6f84b56 /man | |
parent | 92c3caebef9ae7bb361d6811589d3fd6e5c0430a (diff) | |
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adding documentation for iv routine
Diffstat (limited to 'man')
-rw-r--r-- | man/iv.Rd | 64 |
1 files changed, 64 insertions, 0 deletions
diff --git a/man/iv.Rd b/man/iv.Rd new file mode 100644 index 0000000..54d44d2 --- /dev/null +++ b/man/iv.Rd @@ -0,0 +1,64 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/iv.R +\name{iv} +\alias{iv} +\title{ARX model estimation using instrumental variable method} +\usage{ +iv(z, order = c(0, 1, 0), x = NULL) +} +\arguments{ +\item{z}{an idframe object containing the data} + +\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{x}{instrument variable matrix. x must be of the same size as the output +data. (Default: \code{NULL})} +} +\value{ +An object of class \code{estpoly} containing the following elements: + \item{sys}{an \code{idpoly} object containing the + fitted ARX coefficients} + \item{fitted.values}{the predicted response} + \item{residuals}{the residuals} + \item{input}{the input data used} + \item{call}{the matched call} + \item{stats}{A list containing the following fields: \cr + \code{vcov} - the covariance matrix of the fitted coefficients \cr + \code{sigma} - the standard deviation of the innovations\cr + \code{df} - the residual degrees of freedom} +} +\description{ +Estimates an ARX model of the specified order from input-output data using +the instrument variable method. If arbitrary instruments are not supplied +by the user, the instruments are generated using the arx routine +} +\details{ +SISO ARX models are of the form +\deqn{ + y[k] + a_1 y[k-1] + \ldots + a_{na} y[k-na] = b_{nk} u[k-nk] + + \ldots + b_{nk+nb} u[k-nk-nb] + e[k] +} +The function estimates the coefficients using linear least squares (with +regularization). +\cr +The data is expected to have no offsets or trends. They can be removed +using the \code{\link{detrend}} function. +} +\examples{ +data(arxsim) +mod_iv <- iv(z,c(2,1,1)) + +} +\references{ +Arun K. Tangirala (2015), \emph{Principles of System Identification: +Theory and Practice}, CRC Press, Boca Raton. Sections 21.7.1, 21.7.2 + +Lennart Ljung (1999), \emph{System Identification: Theory for the User}, +2nd Edition, Prentice Hall, New York. Section 7.6 +} +\seealso{ +arx +} + |