R: Summarizing Linear Model Fits

lm.summary {base}

R Documentation

Summarizing Linear Model Fits

Description

summary method for class "lm".

Usage

## S3 method for class 'lm'
summary(object, correlation = FALSE, ...)

## S3 method for class 'summary.lm'
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 \times 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 `\hat\sigma^2 = \frac{1}{n-p}\sum_i{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`	(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”, `R^2 = 1 - \frac{\sum_i{R_i^2}}{\sum_i(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 \times p` matrix of (unscaled) covariances of the `\hat\beta_j`, `j=1, \dots, p`.
`correlation`	the correlation matrix corresponding to the above `cov.unscaled`, if `correlation = TRUE` is specified.

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

[Package base version 1.5.0 ]