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 *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 (*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 2.2.1

Index]