addreg (version 3.0)

summary.addreg: Summarizing addreg Model Fits

Description

These functions are all methods for class addreg or summary.addreg objects.

Usage

# S3 method for addreg
summary(object, correlation = FALSE, ...)
# S3 method for summary.addreg
print(x, digits = max(3L, getOption("digits") - 3L), 
      signif.stars = getOption("show.signif.stars"), ...)

Arguments

object

an object of class "addreg", usually from a call to addreg or addreg.smooth.

x

an object of class "summary.addreg", usually from a call to summary.addreg.

correlation

logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.

digits

the number of significant digits to use when printing.

signif.stars

logical; if TRUE, `significance stars' are printed for each coefficient.

…

further arguments passed to or from other methods.

Value

summary.addreg returns an object of class "summary.addreg", a list with components

call

the component from object.

family

the component from object.

deviance

the component from object.

aic

the component from object.

aic.c

the component from object.

df.residual

the component from object.

null.deviance

the component from object.

df.null

the component from object.

iter

the component from object.

deviance.resid

the deviance residuals: see residuals.glm.

coefficients

the matrix of coefficients, standard errors, z-values and p-values.

aliased

included for compatibility --- always FALSE.

dispersion

the inferred/estimated dispersion.

df

included for compatibility --- a 3-vector of the number of coefficients, the number of residual degrees of freedom, and the number of coefficients (again).

cov.unscaled

the unscaled (dispersion = 1) estimated covariance matrix of the estimated coefficients. NaN if object$boundary == TRUE.

cov.scaled

ditto, scaled by dispersion.

correlation

if correlation is TRUE, the estimated correlations of the estimated coefficients. NaN if object$boundary == TRUE.

For negative binomial models, the object also contains

phi

the estimate of \(\phi\) (scale-1).

var.phi

the estimated variance of phi.

Details

These perform the same function as summary.glm and print.summary.glm, producing similar results for addreg models. print.summary.addreg additionally prints the small-sample corrected AIC (aic.c), the number of EM iterations for the parameterisation corresponding to the MLE, and for negative binomial models, the estimate of \(\phi\) (scale-1) and its standard error.

The dispersion used in calculating standard errors is fixed as \(1\) for binomial and Poisson models, and is estimated via maximum likelihood for negative binomial models.

See Also

addreg, summary.glm

Examples

# NOT RUN {
## For an example, see example(addreg)
# }