lsfit {stats} | R Documentation |

## Find the Least Squares Fit

### Description

The least squares estimate of ** \beta** in the model

`\bold{Y} = \bold{X \beta} + \bold{\epsilon}`

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.

### Value

A list with the following named components:

`coef` |
the least squares estimates of the coefficients in
the model ( |

`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)
```

*stats*version 4.4.0 Index]