estARX documentation

1 files changed, 16 insertions, 6 deletions

diff --git a/man/estARX.Rd b/man/estARX.Rd
index d4ff9b6..11858f8 100644
--- a/man/estARX.Rd
+++ b/man/estARX.Rd
@@ -22,8 +22,8 @@ the following elements:
    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{df} \tab the residual degrees of freedom \cr
+   \code{fitted.values} \tab the predicted response \cr
    \code{residuals} \tab the residuals  \cr
    \code{call} \tab the matched call \cr
    \code{time} \tab the time of the data used \cr
@@ -33,7 +33,17 @@ the following elements:
 Fit an ARX model of the specified order given the input-output data
 }
 \details{
-ARX models are of the form \\
+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
+no regularization). Future versions may include regularization
+parameters as well
+\\
+The data is expected to have no offsets or trends. They can be removed
+using the \code{\link{detrend}} function.
 }
 \examples{
 data(arxsim)
@@ -42,10 +52,10 @@ 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
+Arun K. Tangirala (2015), \emph{Principles of System Identification:
+Theory and Practice}, CRC Press, Boca Raton. Section 21.6.1
 
-Lennart Ljung (1999) System Identification: Theory for the User,
+Lennart Ljung (1999), \emph{System Identification: Theory for the User},
 2nd Edition, Prentice Hall, New York. Section 10.1
 }