summary.lm | R Documentation |
summary
method for class "lm"
.
## S3 method for class 'lm' summary(object, correlation = FALSE, symbolic.cor = FALSE, ...) ## S3 method for class 'summary.lm' print(x, digits = max(3, getOption("digits") - 3), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)
object |
an object of class |
x |
an object of class |
correlation |
logical; if |
digits |
the number of significant digits to use when printing. |
symbolic.cor |
logical. If |
signif.stars |
logical. If |
... |
further arguments passed to or from other methods. |
print.summary.lm
tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
‘significance stars’ if signif.stars
is TRUE
.
Aliased coefficients are omitted in the returned object but restored
by the print
method.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$correlation
directly.
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
|
coefficients |
a p x 4 matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value. Aliased coefficients are omitted. |
aliased |
named logical vector showing if the original coefficients are aliased. |
sigma |
the square root of the estimated variance of the random error σ^2 = 1/(n-p) Sum(w[i] R[i]^2), where R[i] is the i-th residual, |
df |
degrees of freedom, a 3-vector (p, n-p, p*), the first being the number of non-aliased coefficients, the last being the total number of coefficients. |
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 - 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
|
symbolic.cor |
(only if |
na.action |
from |
The model fitting function lm
, summary
.
Function coef
will extract the matrix of coefficients
with standard errors, t-statistics and p-values.
##-- 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 ## model with *aliased* coefficient: lm.D9. <- lm(weight ~ group I(group != "Ctl")) Sm.D9. <- summary(lm.D9.) Sm.D9. # shows the NA NA NA NA line stopifnot(length(cc <- coef(lm.D9.)) == 3, is.na(cc[3]), dim(coef(Sm.D9.)) == c(2,4), Sm.D9.$df == c(2, 18, 3))
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