R: Find the Least Squares Fit

lsfit {stats} | R Documentation

## Find the Least Squares Fit

### Description

The least squares estimate of b in the model

y = X b + e

is found.

### Usage

    lsfit(x, y, wt = NULL, 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 a matrix 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` | names to be used for the response variables.

### Details

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 (b as stated above).
`residuals` | residuals from the fit.
`intercept` | indicates whether an intercept was fitted.
`qr` | the QR decomposition of the design matrix.

### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

### See Also

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

### Examples

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

[Package stats version 2.15.1 Index]