Description Usage Arguments Details Value Warnings See Also Examples
These functions are all methods
for class wle.glm
or
summary.wle.glm
objects.
1 2 3 4 5 6 7 8 | ## S3 method for class 'wle.glm'
summary(object, root = 1, dispersion = NULL,
correlation = FALSE, symbolic.cor = FALSE, ...)
## S3 method for class 'summary.wle.glm'
print(x, digits = max(3, getOption("digits") - 3),
symbolic.cor = x$symbolic.cor,
signif.stars = getOption("show.signif.stars"), ...)
|
object |
an object of class |
root |
an integer number to specify for which root the summary should be reported. |
x |
an object of class |
dispersion |
the dispersion parameter for the family used.
Either a single numerical value or |
correlation |
logical; if |
digits |
the number of significant digits to use when printing. |
symbolic.cor |
logical. If |
signif.stars |
logical. If |
... |
further arguments passed to or from other methods. |
print.summary.wle.glm
tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
‘significance stars’ if signif.stars
is TRUE
.
The coefficients
component of the result gives the estimated
coefficients and their estimated standard errors, together with their
ratio. This third column is labelled t ratio
if the
dispersion is estimated, and z ratio
if the dispersion is known
(or fixed by the family). A fourth column gives the two-tailed
p-value corresponding to the t or z ratio based on a Student t or
Normal reference distribution. (It is possible that the dispersion is
not known and there are no residual degrees of freedom from which to
estimate it. In that case the estimate is NaN
.)
Aliased coefficients are omitted in the returned object but restored
by the print
method.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$correlation
directly.
The dispersion of a GLM is not used in the fitting process, but it is
needed to find standard errors.
If dispersion
is not supplied or NULL
,
the dispersion is taken as 1
for the binomial
and
Poisson
families, and otherwise estimated by the residual
Chisquared statistic (calculated from cases with non-zero weights)
divided by the residual degrees of freedom.
summary
can be used with Gaussian wle.glm
fits to handle the
case of a linear regression with known error variance, something not
handled by summary.wle.lm
.
summary.wle.glm
returns an object of class "summary.wle.glm"
, a
list with components
call |
the component from |
family |
the component from |
deviance |
the component from |
contrasts |
the component from |
df.residual |
the component from |
null.deviance |
the component from |
df.null |
the component from |
deviance.resid |
the deviance residuals:
see |
coefficients |
the matrix of coefficients, standard errors, z-values and p-values. Aliased coefficients are omitted. |
aliased |
named logical vector showing if the original coefficients are aliased. |
dispersion |
either the supplied argument or the inferred/estimated
dispersion if the latter is |
df |
a 3-vector of the rank of the model and the number of residual degrees of freedom, plus number of non-aliased coefficients. |
cov.unscaled |
the unscaled ( |
cov.scaled |
ditto, scaled by |
correlation |
(only if |
symbolic.cor |
(only if |
Since in a model selection procedure and/or on an ANOVA table the weights of the WLE procedure must be that of the FULL model (and not that of the actual model) statistics on degrees of freedom, deviance and AIC are valid only if this is the FULL model.
1 2 3 | ## --- Continuing the Example from '?wle.glm':
summary(wle.glm.D93)
|
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