summary.lm {stats} | R Documentation |

`summary`

method for class `"lm"`

.

## S3 method for class 'lm': summary(object, correlation = FALSE, symbolic.cor = FALSE, ...) ## S3 method for class 'summary.lm': print(x, digits = max(3, getOption("digits") - 3), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)

`object` |
an object of class `"lm"` , usually, a result of a
call to `lm` . |

`x` |
an object of class `"summary.lm"` , usually, a result of a
call to `summary.lm` . |

`correlation` |
logical; if `TRUE` , the correlation matrix of
the estimated parameters is returned and printed. |

`digits` |
the number of significant digits to use when printing. |

`symbolic.cor` |
logical. If `TRUE` , print the correlations in
a symbolic form (see `symnum` ) rather than as numbers. |

`signif.stars` |
logical. If `TRUE` , “significance stars”
are printed for each coefficient. |

`...` |
further arguments passed to or from other methods. |

`print.summary.lm`

tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
“significance stars” if `signif.stars`

is `TRUE`

.

Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print `summary(object)$correlation`

directly.

The function `summary.lm`

computes and returns a list of summary
statistics of the fitted linear model given in `object`

, using
the components (list elements) `"call"`

and `"terms"`

from its argument, plus

`residuals` |
the weighted residuals, the usual residuals
rescaled by the square root of the weights specified in the call to
`lm` . |

`coefficients` |
a p x 4 matrix with columns for
the estimated coefficient, its standard error, t-statistic and
corresponding (two-sided) p-value. Aliased coefficients are omitted. |

`aliased` |
named logical vector showing if the original coefficients are aliased. |

`sigma` |
the square root of the estimated variance of the random
error
where |

`df` |
degrees of freedom, a 3-vector (p, n-p, p*), the last
being the number of non-aliased coefficients. |

`fstatistic` |
(for models including non-intercept terms) a 3-vector with the value of the F-statistic with its numerator and denominator degrees of freedom. |

`r.squared` |
R^2, the “fraction of variance explained by
the model”,
where |

`adj.r.squared` |
the above R^2 statistic
“adjusted”, penalizing for higher p. |

`cov.unscaled` |
a p x p matrix of (unscaled)
covariances of the coef[j], j=1, ..., p. |

`correlation` |
the correlation matrix corresponding to the above
`cov.unscaled` , if `correlation = TRUE` is specified. |

`symbolic.cor` |
(only if `correlation` is true.) The value
of the argument `symbolic.cor` . |

The model fitting function `lm`

, `summary`

.

Function `coef`

will extract the matrix of coefficients
with standard errors, t-statistics and p-values.

##-- Continuing the lm(.) example: coef(lm.D90)# the bare coefficients sld90 <- summary(lm.D90 <- lm(weight ~ group -1))# omitting intercept sld90 coef(sld90)# much more

[Package *stats* version 2.1.0 Index]