lm.summary               package:base               R Documentation

Summarizing Linear Model Fits

Description:

     `summary' method for class `"lm"'.

Usage:

     summary(object, correlation = FALSE, ...)

     print(x, digits = max(3, getOption("digits") - 3),
           symbolic.cor = p > 4,
           signif.stars = getOption("show.signif.stars"), ...)

Arguments:

  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.

Details:

     `print.summary.lm' tries to be smart about formatting the
     coefficients, standard errors, etc. and additionally gives
     ``significance stars'' if `signif.stars' is `TRUE'.

Value:

     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.

   sigma: the square root of the estimated variance of the random error

                    sigma^2 = 1/(n-p) Sum(R[i]^2),

          where R[i] is the i-th residual, `residuals[i]'.

      df: degrees of freedom, a 3-vector (p, n-p, p*).

fstatistic: 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'',

              R^2 = 1 - Sum(R[i]^2) / Sum((y[i]- y*)^2),

          where y* is the mean of y[i] if there is an intercept and
          zero otherwise.

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.

See Also:

     The model fitting function `lm', `summary'.

Examples:

     ##-- 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

