summary.glarma {glarma}

Summarize GLARMA Fit

Description

summary method for class glarma and functions to generate the estimates for this summary method.

Usage

## S3 method for class 'glarma'
summary(object, tests = TRUE, ...)
## S3 method for class 'summary.glarma'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
glarmaModelEstimates(object)

Arguments

object

An object of class "glarma", obtained from a call to glarma.

x

An object of class "summary.glarma", obtained from a call to summary.glarma.

digits

Numeric; minimum number of significant digits to be used for most numbers.

tests

Logical; if TRUE, the likelihood-ratio test and the Wald test are shown in the summary. The default is TRUE.

...

Further arguments passed to or from other methods.

Value

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

call

the component from object

null.deviance

null deviance of the GLM with the same regression structure as the GLARMA model.

df.null

null degrees of freedom of the GLM with the same regression structure as the GLARMA model.

phi.lags

the component from object.

theta.lags

the component from object.

pq

the component from object.

iter

the component from object.

deviance

the deviance of the fitted model.

df.residual

the degrees of freedom of the fitted model.

deviance.resid

the component from object.

aic

the component from object.

methods

vector specifying the count distribution of the GLARMA model, the iteration method and the type of residual used.

tests

whether tests were asked for.

likTests

if tests is TRUE, the result of a call to likTests, NULL otherwise.

coefficients1

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

coefficients2

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

coefficients3

when the count distribution is negative binomial, a matrix with 1 row, giving the negative binomial parameter, its standard error, z-ratio and p-value.

Author(s)

"William T.M. Dunsmuir" <[e-mail]> and "Cenanning Li" <[e-mail]>

See Also

glarma, summary.

Examples

## For examples see example(glarma)