# lsfit {stats}

### 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 *j*th case specified by the *j*th 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.

### Values

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.

### Examples

Documentation reproduced from R 3.0.2. License: GPL-2.