R: Summarize UNIDIFF Model Fits

summary.unidiff {logmult}

R Documentation

Summarize UNIDIFF Model Fits

Description

summary method for objects of class unidiff.

Usage

## S3 method for class 'unidiff'
summary(object, ...)

## S3 method for class 'summary.unidiff'
print(x, digits = max(3, getOption("digits") - 4), ...)

Arguments

`object`	an object resulting from a call to `unidiff`
`x`	an object of class `summary.gnm`.
`digits`	the number of siginificant digits to use when printing.
`...`	further arguments passed to `printCoefmat` by `print.summary.unidiff`, and currently ignored by `summary.unidiff`.

Details

print.summary.unidiff prints the original call to unidiff; a summary of the deviance residuals from the model fit; the coefficients of interest of the model; the residual deviance; the residual degrees of freedom; the Schwartz's Bayesian Information Criterion value; the Akaike's An Information Criterion value.

Layer and two-way interaction coefficients are printed with their standard errors, quasi-standard errors (see qvcalc), p-values (based on standard errors) and significance stars. Constrained coefficients have a value of 0 (by default), and 0 standard errors, but still have quasi-standard errors.

Value

An object of class summary.unidiff, with the following components:

`call`	the `call` component from object.
`deviance.resid`	the deviance residuals, see `residuals.glm.`
`layer`	a `data.frame` holding the layer coefficients estimates, standard errors, quasi-standard errors (see `qvcalc`) and p-values.
`phi.layer`	a `data.frame` holding the layer coefficients estimates, standard errors, and quasi-standard errors (see `qvcalc`) multiplied by the intrinsic association coefficient (see `maor`) for the first layer; p-values are the same as those for the “layer” component.
`interaction`	a `data.frame` holding the two-way interaction coefficients estimates, standard errors and p-values.
`deviance`	the `deviance` component from object.
`diagonal`	the `diagonal` component from the object's `unidiff` component.
`weighting`	the `weighting` component from the object's `unidiff` component.
`chisq`	the Pearson Chi-squared statistic for the model fit.
`dissim`	the dissimilarity index for the model fit.
`df.residual`	the `df.residual` component from object.
`bic`	the value of the BIC for the model fit (contrary to the value reported by `AIC` and `extractAIC`, the reference is 0 for the saturated model).
`aic`	the value of the AIC for the model fit (contrary to the value reported by `AIC` and `extractAIC`, the reference is 0 for the saturated model).
`family`	the `family` component from object.
`dispersion`	the estimated dispersion
`df`	a 3-vector of the rank of the model; the number of residual degrees of freedom; and number of unconstrained coefficients.

Author(s)

Milan Bouchet-Valat