summary.logbin: Summarising logbin Model Fits

In logbin: Relative Risk Regression Using the Log-Binomial Model

Description

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

Usage

## S3 method for class 'logbin'
summary(object, correlation = FALSE, ...)

## S3 method for class 'summary.logbin'
print(x, digits = max(3L, getOption("digits") - 3L), 
      signif.stars = getOption("show.signif.stars"), ...)

Arguments

object	an object of class "logbin", usually from a call to logbin or logbin.smooth.
x	an object of class "summary.logbin", usually from a call to summary.logbin.
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.

Details

These perform the same function as summary.glm and print.summary.glm, producing similar results for logbin models. print.summary.logbin additionally prints the small-sample corrected AIC (aic.c), and the number of EM iterations for the parameterisation corresponding to the MLE.

The dispersion used in calculating standard errors is fixed as 1.

Value

summary.logbin returns an object of class "summary.logbin", 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.

Note

If object$boundary == TRUE, the standard errors of the coefficients are not valid, and a matrix of NaNs is returned by vcov.logbin. If the MLE is not on the boundary but the model contains parameters with monotonicity constraints, the standard errors do not take this into account and should be used with caution.

Author(s)

Mark W. Donoghoe [e-mail]

See Also

logbin, summary.glm

Examples

## For examples see example(logbin)

Example output

logbin documentation built on Aug. 10, 2021, 1:06 a.m.