summary.lm
Summarizing Linear Model Fits
summary
method for class "lm"
.
- Keywords
- models, regression
Usage
"summary"(object, correlation = FALSE, symbolic.cor = FALSE, ...)
"print"(x, digits = max(3, getOption("digits") - 3), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)
Arguments
- object
- an object of class
"lm"
, usually, a result of a call tolm
. - x
- an object of class
"summary.lm"
, usually, a result of a call tosummary.lm
. - correlation
- logical; if
TRUE
, the correlation matrix of the estimated parameters is returned and printed. - digits
- the number of significant digits to use when printing.
- symbolic.cor
- logical. If
TRUE
, print the correlations in a symbolic form (seesymnum
) rather than as numbers. - signif.stars
- logical. If
TRUE
, significance stars are printed for each coefficient. - ...
- further arguments passed to or from other methods.
Details
print.summary.lm
tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
significance stars if signif.stars
is TRUE
.
Aliased coefficients are omitted in the returned object but restored
by the print
method.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$correlation
directly.
Value
-
The function
- 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
$$\hat\sigma^2 = \frac{1}{n-p}\sum_i{w_i R_i^2},$$
where $R[i]$ is the $i$-th residual,
residuals[i]
. - 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, $$R^2 = 1 - \frac{\sum_i{R_i^2}}{\sum_i(y_i- y^*)^2},$$ 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 $coef[j]$, $j=1, \dots, p$.
- correlation
- the correlation matrix corresponding to the above
cov.unscaled
, ifcorrelation = TRUE
is specified. - symbolic.cor
- (only if
correlation
is true.) The value of the argumentsymbolic.cor
. - na.action
- from
object
, if present there.
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
See Also
The model fitting function lm
, summary
.
Function coef
will extract the matrix of coefficients
with standard errors, t-statistics and p-values.
Examples
library(stats)
##-- 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))
Community examples
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