lm.summaries {base} | R Documentation

## Accessing Linear Model Fits

### Description

All these functions are `methods` for class `lm` or `summary.lm` objects.

### Usage

    summary(object, correlation = FALSE)
    coefficients(object, ...) ; coef(object, ...)
    df.residual(object, ...)
    family(object, ...)
    formula(x, ...)
    fitted.values(object, ...)
    residuals(object,
              type=c("working","response", "deviance","pearson", "partial"), ...)
    weights(object, ...)

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

### Arguments

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

### 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`.

The generic accessor functions `coefficients`, `effects`, `fitted.values` and `residuals` can be used to extract various useful features of the value returned by `lm`.

### Value

The function `summary.lm` computes and returns a list of summary statistics of the fitted linear model given in `lm.obj`, 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`, `anova.lm`.

`coefficients`, `deviance`, `effects`, `fitted.values`, `glm` for generalized linear models, `lm.influence` for regression diagnostics, `weighted.residuals`, `residuals`, `residuals.glm`, `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

    ## The 2 basic regression diagnostic plots [plot.lm(.) is preferred]
    plot(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's
    abline(h=0, lty=2, col = 'gray')

    qqnorm(residuals(lm.D90))