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Diffstat (limited to 'man/armax.Rd')
| -rw-r--r-- | man/armax.Rd | 27 |
1 files changed, 14 insertions, 13 deletions
diff --git a/man/armax.Rd b/man/armax.Rd index fc2388a..a7c92e4 100644 --- a/man/armax.Rd +++ b/man/armax.Rd @@ -1,4 +1,4 @@ -% Generated by roxygen2 (4.1.1): do not edit by hand +% Generated by roxygen2: do not edit by hand % Please edit documentation in R/estpoly.R \name{armax} \alias{armax} @@ -11,15 +11,15 @@ armax(x, order = c(0, 1, 1, 0), options = optimOptions()) \item{options}{Estimation Options, setup using \code{\link{optimOptions}}} -\item{order:}{Specification of the orders: the four integer components -(na,nb,nc,nk) are the order of polynolnomial A, order of polynomial B +\item{order:}{Specification of the orders: the four integer components +(na,nb,nc,nk) are the order of polynolnomial A, order of polynomial B + 1, order of the polynomial C,and the input-output delay respectively} } \value{ An object of class \code{estpoly} containing the following elements: \tabular{ll}{ - \code{sys} \tab an \code{idpoly} object containing the + \code{sys} \tab an \code{idpoly} object containing the fitted ARMAX coefficients \cr \code{fitted.values} \tab the predicted response \cr \code{residuals} \tab the residuals \cr @@ -30,31 +30,31 @@ An object of class \code{estpoly} containing the following elements: \code{vcov} \tab the covariance matrix of the fitted coefficients\cr \code{sigma} \tab the standard deviation of the innovations } \cr - \code{options} \tab Option set used for estimation. If no + \code{options} \tab Option set used for estimation. If no custom options were configured, this is a set of default options. \cr \code{termination} \tab Termination conditions for the iterative search used for prediction error minimization. \tabular{ll}{ \code{WhyStop} \tab Reason for termination \cr \code{iter} \tab Number of Iterations \cr - \code{iter} \tab Number of Function Evaluations - } + \code{iter} \tab Number of Function Evaluations + } } } \description{ Fit an ARMAX model of the specified order given the input-output data } \details{ -SISO ARMAX models are of the form +SISO ARMAX models are of the form \deqn{ - y[k] + a_1 y[k-1] + \ldots + a_{na} y[k-na] = b_{nk} u[k-nk] + + 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] + c_{1} e[k-1] + \ldots c_{nc} e[k-nc] - + e[k] + + e[k] } -The function estimates the coefficients using non-linear least squares +The function estimates the coefficients using non-linear least squares (Levenberg-Marquardt Algorithm) \\ -The data is expected to have no offsets or trends. They can be removed +The data is expected to have no offsets or trends. They can be removed using the \code{\link{detrend}} function. } \examples{ @@ -63,9 +63,10 @@ z <- dataSlice(data,end=1533) # training set mod_armax <- armax(z,c(1,2,1,2)) summary(mod_armax) # obtain estimates and their covariances plot(mod_armax) # plot the predicted and actual responses + } \references{ -Arun K. Tangirala (2015), \emph{Principles of System Identification: +Arun K. Tangirala (2015), \emph{Principles of System Identification: Theory and Practice}, CRC Press, Boca Raton. Sections 14.4.1, 21.6.2 } |