Improve \usage.

git-svn-id: https://svn.r-project.org/R/trunk@15342 00db46b3-68df-0310-9c12-caf00c1e9a41

src/library/eda/man/line.Rd

@@ -1,20 +1,20 @@
 \name{line}
-\title{Robust Line Fitting}
-\usage{
-line(x, y)
-
-coefficients(tukeyline.obj)
-residuals(tukeyline.obj)
-fitted.values(tukeyline.obj)
-print(tukeyline.obj)
-}
 \alias{line}
-\alias{coefficients.tukeyline}
+\alias{coeff.tukeyline}
 \alias{residuals.tukeyline}
-\alias{fitted.values.tukeyline}
+\alias{fitted.tukeyline}
 \alias{print.tukeyline}
 \alias{coef.tukeyline}
 \alias{fitted.tukeyline}
+\title{Robust Line Fitting}
+\usage{
+line(x, y)
+
+\method{coef}{tukeyline}(tukeyline.obj)
+\method{residuals}{tukeyline}(tukeyline.obj)
+\method{fitted}{tukeyline}(tukeyline.obj)
+\method{print}{tukeyline}(tukeyline.obj)
+}
 \arguments{
   \item{x,y}{the arguments can be any way of specifying x-y pairs.}
 }

src/library/eda/man/medpolish.Rd

@@ -1,14 +1,17 @@
 \name{medpolish}
+\alias{medpolish}
+\alias{plot.medpolish}
+\alias{print.medpolish}
 \title{Median Polish of a Matrix}
+\description{
+  Fits an additive model using Tukey's \emph{median polish} procedure.
+}
 \usage{
 medpolish(x, eps = 0.01, maxiter = 10, trace.iter = TRUE)
 
-plot(medpolish.obj)
-print(medpolish.obj)
+\method{plot}{medpolish}(medpolish.obj)
+\method{print}{medpolish}(medpolish.obj)
 }
-\alias{medpolish}
-\alias{plot.medpolish}
-\alias{print.medpolish}
 \arguments{
   \item{x}{a numeric matrix.}
   \item{eps}{real number greater than 0. A tolerance for convergence:
@@ -17,9 +20,6 @@ print(medpolish.obj)
   \item{trace.iter}{logical. Should progress in convergence be reported?}
   \item{medpolish.obj}{object of class \code{medpolish}.}
 }
-\description{
-  Fits an additive model using Tukey's \emph{median polish} procedure.
-}
 \details{
   The model fitted is additive (constant + rows + columns). The
   algorithm works by alternately removing the row and column medians,

src/library/eda/man/smooth.Rd

@@ -1,8 +1,8 @@
 \name{smooth}
-\title{Median Smoothing}
 \alias{smooth}
 \alias{print.tukeysmooth}
 \alias{summary.tukeysmooth}
+\title{Median Smoothing}
 \description{
   Tukey's smoothers, \emph{3RS3R}, \emph{3RSS}, \emph{3R}, etc.
 }
@@ -11,8 +11,8 @@ smooth(x, kind = c("3RS3R", "3RSS", "3RSR", "3R", "3", "S"),
        twiceit = FALSE,
        endrule = "Tukey", do.ends = FALSE)
 
-  print(smoothobj, \dots)
-summary(smoothobj, \dots)
+\method{print}{tukeysmooth}(smoothobj, \dots)
+\method{summary}{tukeysmooth}(smoothobj, \dots)
 }
 \arguments{
   \item{x}{a vector or time series}

src/library/mva/man/princomp.Rd

@@ -14,8 +14,10 @@ loadings(x)
 
 screeplot(x, npcs = min(10, length(x$sdev)),
           type = c("barplot", "lines"), main = deparse(substitute(x)), \dots)
-plot(x, \dots)
-print(x, \dots)  summary(object)  predict(object, \dots)
+\method{plot}{princomp}(x, \dots)
+\method{print}{princomp}(x, \dots)
+\method{summary}{princomp}(object)
+\method{predict}{princomp}(object, \dots)
 }
 \arguments{
   \item{x}{a matrix (or data frame) which provides the data for the

src/library/nls/man/formula.nls.Rd

@@ -1,10 +1,10 @@
-% $Id: formula.nls.Rd,v 1.3 2001/03/24 06:57:57 ripley Exp $
+% $Id: formula.nls.Rd,v 1.4 2001/08/08 08:21:08 hornik Exp $
 \name{formula.nls}
+\alias{formula.nls}
 \title{Extract Model Formula from nls Object}
 \usage{
-formula(object)
+\method{formula}{nls}(object)
 }
-\alias{formula.nls}
 \arguments{
  \item{object}{an object inheriting from class \code{nls}, representing
    a nonlinear least squares fit.}

src/library/stepfun/man/ecdf.Rd

@@ -1,13 +1,13 @@
 \name{ecdf}
-\title{Empirical Cumulative Distribution Function}
-\usage{
-ecdf(x)
-plot(\dots, verticals = FALSE, col.01line = "gray70")
-}
 \alias{ecdf}
 \alias{print.ecdf}
 \alias{summary.ecdf}
 \alias{plot.ecdf}
+\title{Empirical Cumulative Distribution Function}
+\usage{
+ecdf(x)
+\method{plot}{ecdf}(\dots, verticals = FALSE, col.01line = "gray70")
+}
 \arguments{
   \item{x}{numeric vector with the ``observations''.}
   \item{\dots}{arguments to be passed to \code{\link{plot.stepfun}}, the

src/library/ts/man/ar.Rd

@@ -21,7 +21,7 @@ ar(x, aic = TRUE, order.max = NULL,
 ar.mle(x, aic = TRUE, order.max = NULL, na.action, demean = TRUE,
        series, \dots)
 
-predict(ar.obj, newdata, n.ahead = 1, se.fit = TRUE)
+\method{predict}{ar}(object, newdata, n.ahead = 1, se.fit = TRUE, \dots)
 }
 \arguments{
   \item{x}{A univariate or multivariate time series.}
@@ -51,7 +51,7 @@ predict(ar.obj, newdata, n.ahead = 1, se.fit = TRUE)
   \item{var.method}{the method to estimate the innovations variance
     (see Details).}
 
-  \item{...}{additional arguments for specific methods.}
+  \item{\dots}{additional arguments for specific methods.}
 
   \item{ar.obj}{a fit from \code{ar}.}
 

src/library/ts/man/arima0.Rd

@@ -10,7 +10,7 @@ arima0(x, order = c(0, 0, 0),
        xreg = NULL, include.mean, na.action = na.fail,
        delta = 0.01, transform.pars = 2)
 
-predict(arima0.obj, n.ahead = 1, newxreg, se.fit = TRUE)
+\method{predict}{arima0}(object, n.ahead = 1, newxreg, se.fit = TRUE)
 
 arima0.diag(fit, gof.lag = 10) 
 }
@@ -42,7 +42,7 @@ arima0.diag(fit, gof.lag = 10)
     invertibility. If equal to 2, the optimization is rerun on
     the original scale to find the Hessian.}
 
-  \item{arima0.obj, fit}{The result of an \code{arima0} fit.}
+  \item{object, fit}{The result of an \code{arima0} fit.}
 
   \item{newxreg}{New values of \code{xreg} to be used for
     prediction. Must have at least \code{n.ahead} rows.}

src/library/ts/man/kernel.Rd

@@ -6,17 +6,21 @@
 \alias{print.tskernel}
 \alias{plot.tskernel}
 \alias{[.tskernel}
-
 \title{Smoothing Kernel Objects}
+\description{
+  The \code{"tskernel"} class is designed to represent discrete
+  symmetric normalized smoothing kernels.  These kernels can be used to
+  smooth vectors, matrices, or time series objects.
+}
 \usage{
 kernel(coef, m, r, name)
 
 df.kernel(k)
 bandwidth.kernel(k)
 is.tskernel(k)
 
-print(k, digits = max(3,getOption("digits")-3))
-plot(k)
+\method{print}{tskernel}(k, digits = max(3,getOption("digits")-3))
+\method{plot}{tskernel}(k)
 }
 \arguments{
   \item{coef}{the upper half of the smoothing kernel coefficients
@@ -29,11 +33,6 @@ plot(k)
   \item{r}{the kernel order for a Fejer kernel.}
   \item{digits}{the number of digits to format real numbers.}
 }
-\description{
-  The \code{"tskernel"} class is designed to represent discrete symmetric
-  normalized smoothing kernels. These kernels can be used to smooth
-  vectors, matrices, or time series objects.
-}
 \details{
   \code{kernel} is used to construct a general kernel or
   named specific kernels. The modified Daniell kernel

src/library/ts/man/lag.Rd

@@ -2,18 +2,18 @@
 \alias{lag}
 \alias{lag.default}
 \title{Lag a Time Series}
+\description{
+  Computed a lagged version of a time series, shifting the time base
+  back by a given number of observations.
+}
 \usage{
-lag(x, ...)
-lag.default(x, k=1)
+lag(x, \dots)
+lag.default(x, k = 1, \dots)
 }
 \arguments{
   \item{x}{A vector or matrix or univariate or multivariate time series}
   \item{k}{The number of lags (in units of observations).}
-  \item{\dots}{Arguments for future methods.}
-}
-\description{
-  Computed a lagged version of a time series, shifting the time base
-  back by \code{k} observations.
+  \item{\dots}{further arguments to be passed to or from methods.}
 }
 \details{
   Vector or matrix arguments \code{x} are coerced to time series.