\Header{lsfit}{Find the Least Squares Fit}
\keyword{regression}{lsfit}
\begin{Description}\relax
The least squares estimate of \bold{\eqn{\beta}{b}} in the model
\deqn{\bold{Y} = \bold{X \beta} + \bold{\epsilon}}{y = X b + e}
is found.\end{Description}
\begin{Usage}
\begin{verbatim}
lsfit(x, y, wt=NULL, intercept=TRUE, tolerance=1e-07, yname=NULL)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] a matrix whose rows correspond to cases and whose columns
correspond to variables.
\item[\code{y}] the responses, possibly matrix valued if you want to fit multiple
left hand sides.
\item[\code{wt}] an optional vector of weights for performing weighted least squares.
\item[\code{intercept}] whether or not an intercept term should be used.
\item[\code{tolerance}] the tolerance to be used in the matrix decomposition.
\item[\code{yname}] an unused parameter for compatibility.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
If weights are specified then a weighted least squares is performed
with the weight given to the \emph{j}th case specified by the \emph{j}th
entry in \code{wt}.

If any observation has a missing value in any field, that observation
is removed before the analysis is carried out.
This can be quite inefficient if there is a lot of missing data.

The implementation is via a modification of the LINPACK subroutines
which allow for multiple left-hand sides.\end{Details}
\begin{Value}
A list with the following named components:
\begin{ldescription}
\item[\code{coef}] the least squares estimates of the coefficients in
the model (stated below).
\item[\code{residuals}] residuals from the fit.
\item[\code{intercept}] indicates whether an intercept was fitted.
\item[\code{qr}] the QR decomposition of the design matrix.
\end{ldescription}
\end{Value}
\begin{SeeAlso}\relax
\code{\Link{lm}} which usually is preferable;
\code{\Link{ls.print}}, \code{\Link{ls.diag}}.\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}

##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = codes(gl(2,10)), y = weight)
ls.print(lsD9)
\end{ExampleCode}
\end{Examples}

