Summarize Generalized Nonlinear Model Fits
summary method for objects of class "gnm"
## S3 method for class 'gnm'
summary(object, dispersion = NULL, correlation = FALSE,
symbolic.cor = FALSE, with.eliminate = FALSE, ...)
## S3 method for class 'summary.gnm'
print(x, digits = max(3, getOption("digits") - 3),
signif.stars = getOption("show.signif.stars"),
symbolic.cor = x$symbolic.cor, ...)object |
an object of class |
x |
an object of class |
dispersion |
the dispersion parameter for the fitting family. By
default it is obtained from |
correlation |
logical: if |
digits |
the number of significant digits to use when printing. |
symbolic.cor |
logical: if |
signif.stars |
logical. If |
with.eliminate |
Logical. If |
... |
further arguments passed to or from other methods. |
print.summary.gnm prints the original call to gnm; a
summary of the deviance residuals from the model fit; the coefficients
of the model; the residual deviance; the Akaike's Information
Criterion value, and the number of main iterations performed.
Standard errors, z-values and p-values are printed alongside the
coefficients, with "significance stars" if signif.stars is
TRUE.
When the "summary.gnm" object has a "correlation"
component, the lower triangle of this matrix is also printed, to two
decimal places (or symbolically); to see the full matrix of
correlations print summary(object, correlation =
TRUE)$correlation directly.
The standard errors returned by summary.gnm are scaled by
sqrt(dispersion). If the dispersion is not specified, it is
taken as 1 for the binomial and Poisson families,
and otherwise estimated by the residual Chi-squared statistic divided
by the residual degrees of freedom. For coefficients that have been
constrained or are not estimable, the standard error is returned as
NA.
summary.gnm returns an object of class "summary.gnm",
which is a list with components
call |
the |
ofInterest |
the |
family |
the |
deviance |
the |
aic |
the |
df.residual |
the |
iter |
the |
deviance.resid |
the deviance residuals, see
|
coefficients |
the matrix of coefficients, standard errors, z-values and p-values. |
elim.coefs |
if |
dispersion |
either the supplied argument or the estimated dispersion if
the latter is |
df |
a 3-vector of the rank of the model; the number of residual degrees of freedom, and number of unconstrained coefficients. |
cov.scaled |
the estimated covariance matrix scaled by
|
correlation |
(only if |
symbolic.cor |
(only if |
The gnm class includes generalized linear models, and it
should be noted that summary.gnm differs from
summary.glm in that it does not omit coefficients which
are NA from the objects it returns. (Such coefficients are
NA since they have been fixed at 0 either by use of the
constrain argument to gnm or by a convention to handle
linear aliasing).
Modification of summary.glm by the R Core Team. Adapted
for "gnm" objects by Heather Turner.
### First example from ?Dref
set.seed(1)
## reconstruct counts voting Labour/non-Labour
count <- with(voting, percentage/100 * total)
yvar <- cbind(count, voting$total - count)
## fit diagonal reference model with constant weights
classMobility <- gnm(yvar ~ -1 + Dref(origin, destination),
family = binomial, data = voting)
## summarize results - note diagonal weights are over-parameterised
summary(classMobility)
## refit setting first weight to zero (as DrefWeights() does)
classMobility <- gnm(yvar ~ -1 + Dref(origin, destination),
family = binomial, data = voting,
constrain = "delta1")
summary(classMobility)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.