lm.fit {stats} | R Documentation |

These are the basic computing engines called by `lm`

used
to fit linear models. These should usually *not* be used
directly unless by experienced users.

lm.fit (x, y, offset = NULL, method = "qr", tol = 1e-7, singular.ok = TRUE, ...) lm.wfit(x, y, w, offset = NULL, method = "qr", tol = 1e-7, singular.ok = TRUE, ...)

`x` |
design matrix of dimension `n * p` . |

`y` |
vector of observations of length `n` , or a matrix with
`n` rows. |

`w` |
vector of weights (length `n` ) to be used in the fitting
process for the `wfit` functions. Weighted least squares is
used with weights `w` , i.e., `sum(w * e^2)` is minimized. |

`offset` |
numeric of length `n` ). This can be used to
specify an a priori known component to be included in the
linear predictor during fitting. |

`method` |
currently, only `method="qr"` is supported. |

`tol` |
tolerance for the `qr` decomposition. Default
is 1e-7. |

`singular.ok` |
logical. If `FALSE` , a singular model is an
error. |

`...` |
currently disregarded. |

a list with components

`coefficients` |
`p` vector |

`residuals` |
`n` vector or matrix |

`fitted.values` |
`n` vector or matrix |

`effects` |
(not null fits)`n` vector of orthogonal single-df
effects. The first `rank` of them correspond to non-aliased
coeffcients, and are named accordingly. |

`weights` |
`n` vector — only for the `*wfit*`
functions. |

`rank` |
integer, giving the rank |

`df.residual` |
degrees of freedom of residuals |

`qr` |
(not null fits) the QR decomposition, see `qr` . |

`lm`

which you should use for linear least squares regression,
unless you know better.

set.seed(129) n <- 7 ; p <- 2 X <- matrix(rnorm(n * p), n,p) # no intercept! y <- rnorm(n) w <- rnorm(n)^2 str(lmw <- lm.wfit(x=X, y=y, w=w)) str(lm. <- lm.fit (x=X, y=y))

[Package *stats* version 2.2.1 Index]