lm.summaries {base} | R Documentation |
All these functions are methods
for class lm
or
summary.lm
objects.
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"), ...)
object, x |
an object of class lm , usually, a result of a
call to lm . |
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
.
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, |
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. |
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
.
##-- 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))