anova(lm.obj, ...) summary(lm.obj, correlation = FALSE) predict(lm.obj,newdata = model.frame(object), conf.level = 0.95, tol.level = conf.level) coefficients(lm.obj) deviance(lm.obj) df.residual(lm.obj) effects(lm.obj) fitted.values(lm.obj) residuals(lm.obj) weights(lm.obj) print.summary(summary.lm.obj, digits = max(3, .Options$digits - 3), symbolic.cor = p > 4, signif.stars= TRUE, ...)
lm.obj
|
an object of class lm , usually, a result of
lm(..) .
|
methods
for class lm
or
summary.lm
and anova.lm
objects.
print.summary.lm
tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives ``significance
stars'' if signif.stars= TRUE
.
summary.lm
computes and returns a list of summary
statistics of the fitted linear model given in lm.obj
, using
the slots (list elements) "call"
, "terms"
, and
"residuals"
from its argument, plus
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.
|
The function anova
produces an analysis of variance
(anova
) table. The generic accessor functions
coefficients
, effects
, fitted.values
and
residuals
can be used to extract various useful features of the
value returned by lm
.
lm
.
anova
for the ANOVA table,
coefficients
, effects
, fitted.values
,
glm
for generalized linear models,
lm.influence
for regression diagnostics,
residuals
, 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(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's abline(h=0, lty=2, col = 'gray') qqnorm(residuals(lm.D90)) ## Predictions x<-rnorm(15) y<-x+rnorm(15) predict(lm(y~x)) predict(lm(y~x),data.frame(x=seq(-3,3,0.1)),.99,.90)