minor documentation updates

1 files changed, 18 insertions, 17 deletions

diff --git a/man/oe.Rd b/man/oe.Rd
index eee4839..42f6aec 100644
--- a/man/oe.Rd
+++ b/man/oe.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{oe}
 \alias{oe}
@@ -9,18 +9,18 @@ oe(x, order = c(1, 1, 0), options = optimOptions())
 \arguments{
 \item{x}{an object of class \code{idframe}}
 
-\item{options}{Estimation Options, setup using
-\code{\link{optimOptions}}}
-
-\item{order:}{Specification of the orders: the four integer components
+\item{order}{Specification of the orders: the four integer components 
 (nb,nf,nk) are order of polynomial B + 1, order of the polynomial F,
 and the input-output delay respectively}
+
+\item{options}{Estimation Options, setup using 
+\code{\link{optimOptions}}}
 }
 \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 OE coefficients \cr
    \code{fitted.values} \tab the predicted response \cr
    \code{residuals} \tab the residuals  \cr
@@ -31,43 +31,44 @@ 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 output-error model of the specified order given the input-output data
 }
 \details{
-SISO OE models are of the form
+SISO OE models are of the form 
 \deqn{
-   y[k] + f_1 y[k-1] + \ldots + f_{nf} y[k-nf] = b_{nk} u[k-nk] +
+   y[k] + f_1 y[k-1] + \ldots + f_{nf} y[k-nf] = b_{nk} u[k-nk] + 
    \ldots + b_{nk+nb} u[k-nk-nb] + f_{1} e[k-1] + \ldots f_{nf} e[k-nf]
-   + 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{
 data(oesim)
 z <- dataSlice(data,end=1533) # training set
 mod_oe <- oe(z,c(2,1,2),optimOptions(tol=1e-04,LMinit=0.01))
-mod_oe
+mod_oe 
 plot(mod_oe) # plot the predicted and actual responses
+
 }
 \references{
-Arun K. Tangirala (2015), \emph{Principles of System Identification:
-Theory and Practice}, CRC Press, Boca Raton. Sections 14.4.1, 17.5.2,
+Arun K. Tangirala (2015), \emph{Principles of System Identification: 
+Theory and Practice}, CRC Press, Boca Raton. Sections 14.4.1, 17.5.2, 
 21.6.3
 }