minor documentation updates

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
 }