| summary.lm {stats} | 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$symbolic.cor,
signif.stars = getOption("show.signif.stars"), ...)
object |
an object of class "lm", usually, a result of a
call to lm. |
x |
an object of class "summary.lm", usually, a result of a
call to summary.lm. |
correlation |
logical; if TRUE, the correlation matrix of
the estimated parameters is returned and printed. |
digits |
the number of significant digits to use when printing. |
symbolic.cor |
logical. If TRUE, print the correlations in
a symbolic form (see symnum) rather than as numbers. |
signif.stars |
logical. If TRUE, “significance stars”
are printed for each coefficient. |
... |
further arguments passed to or from other methods. |
print.summary.lm tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
“significance stars” if signif.stars is TRUE.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$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
lm. |
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
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*), the last being the number of non-aliased 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
cov.unscaled, if correlation = TRUE is specified. |
symbolic.cor |
(only if correlation is true.) The value
of the argument symbolic.cor. |
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