Description Usage Arguments Details Value Note Author(s) See Also Examples
summary
method for objects of class "gnm"
1 2 3 4 5 6 7 8  ## 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, zvalues and pvalues 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 Chisquared 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, zvalues and pvalues. 
elim.coefs 
if 
dispersion 
either the supplied argument or the estimated dispersion if
the latter is 
df 
a 3vector 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ### First example from ?Dref
set.seed(1)
## reconstruct counts voting Labour/nonLabour
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 overparameterised
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)

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