R: Summarize Generalized Nonlinear Model Fits

summary.gnm {gnm} | R Documentation

## Summarize Generalized Nonlinear Model Fits

### Description

`summary` method for objects of class `"gnm"`

### Usage

    ## 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, ...)

### Arguments

`object` | an object of class `"gnm"`.
`x` | an object of class `"summary.gnm"`.
`dispersion` | the dispersion parameter for the fitting family. By default it is obtained from `object`.
`correlation` | logical: if `TRUE`, the correlation matrix of the estimated parameters is returned.
`digits` | the number of significant digits to use when printing.
`symbolic.cor` | logical: if `TRUE`, the correlations are printed in a symbolic form rather than numbers (see `symnum`).
`signif.stars` | logical. If `TRUE`, "significance stars" are printed for each coefficient.
`with.eliminate` | Logical. If `TRUE`, any eliminated coefficients are included in the summary.
`...` | further arguments passed to or from other methods.

### Details

`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`.

### Value

`summary.gnm` returns an object of class `"summary.gnm"`, which is a list with components

`call` | the `"call"` component from object.
`ofInterest` | the `"ofInterest"` component from object.
`family` | the `"family"` component from object.
`deviance` | the `"deviance"` component from object.
`aic` | the `"aic"` component from object.
`df.residual` | the `"df.residual"` component from object.
`iter` | the `"iter"` component from object.
`deviance.resid` | the deviance residuals, see `residuals.glm`.
`coefficients` | the matrix of coefficients, standard errors, z-values and p-values.
`elim.coefs` | if `with.eliminate = TRUE` a matrix of eliminated coefficients, standard errors, z-values and p-values.
`dispersion` | either the supplied argument or the estimated dispersion if the latter is `NULL`.
`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 `dispersion` (see `vcov.gnm` for more details).
`correlation` | (only if `correlation` is `TRUE`) the estimated correlations of the estimated coefficients.
`symbolic.cor` | (only if `correlation` is `TRUE`) the value of the argument `symbolic.cor`.

### Note

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).

### Author(s)

Modification of `summary.glm` by the R Core Team. Adapted for `"gnm"` objects by Heather Turner.

### See Also

`gnm`, `summary`

### Examples

    ### 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)

[Package gnm version 1.1-2 Index]