## Find the Least Squares Fit

## Usage

    lsfit(x, y, wt, intercept=TRUE, tolerance=1e-07, yname=NULL)

## Arguments

`x` | a matrix whose rows correspond to cases and whose columns correspond to variables.
`y` | the responses, possibly matrix valued if you want to fit multiple left hand sides.
`wt` | an optional vector of weights for performing weighted least squares.
`intercept` | whether or not an intercept term should be used.
`tolerance` | the tolerance to be used in the matrix decomposition.
`yname` | an unused parameter for compatibility.

## Description

The least squares estimate of b in the model

y = X b + e

is found. If weights are specified then a weighted least squares is performed with the weight given to the jth case specified by the jth entry in `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.

## Value

A list with the following named components:

`coef` | the least squares estimates of the coefficients in the model (stated below).
`residuals` | residuals from the fit.
`intercept` | indicates whether an intercept was fitted.
`qr` | the QR decomposition of the design matrix.

## See Also

`lm` which usually is preferable; `ls.print`, `ls.diag`, `rreg`.

## Examples

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

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