summary.tsglm {tscount}

Summarising Fits of Count Time Series following Generalised Linear Models

Description

summary method for class "tsglm".

Usage

## S3 method for class 'tsglm'
summary(object, B, parallel=FALSE, level=0.95, ...)

Arguments

object

an object of class "tsglm". Usually the result of a call to tsglm.

B

controls the computation of standard errors. Is passed to se.

parallel

controls the computation of standard errors. Is passed to se.

level

controls the computation of conficence intervals. Is passed to se.

...

further arguments are currently ignored. Only for compatibility with generic function.

Details

Computes and returns a list of summary statistics of the fitted model given in argument object.

Value

A named list with the following elements:

call

see tsglm.

link

see tsglm.

distr

see tsglm.

residuals

see tsglm.

coefficients

data frame with estimated parameters, their standard errors and confidence intervals (based on a normal approximation or a parametric bootstrap, see se.tsglm).

level

numerical value giving the coverage rate of the confidence intervals.

number.coef

number of coefficients.

se.type

type of standard errors, see se.tsglm.

se.bootstrapsamples

number of bootstrap samples used for estimation of the standard errors, see se.tsglm. Is omitted if the standard errors are not obtained by a bootstrap procedure.

logLik

value of the log-likelihood function evaluated at the (quasi) maximum likelihood estimate.

AIC

Akaike's information criterion (AIC), see AIC.

BIC

Bayesian information criterion (BIC), see BIC.

QIC

Quasi information criterion (QIC), see QIC.tsglm.

pearson.resid

Pearson residuals, see residuals.tsglm.

Author(s)

Tobias Liboschik and Philipp Probst

See Also

S3 method print.

tsglm for fitting a GLM for time series of counts.

Examples

###Road casualties in Great Britain (see help("Seatbelts"))
timeseries <- Seatbelts[, "VanKilled"]
regressors <- cbind(PetrolPrice=Seatbelts[, c("PetrolPrice")],
                    linearTrend=seq(along=timeseries)/12)
#Logarithmic link function with Poisson distribution:                    
seatbeltsfit <- tsglm(ts=timeseries, link="log",
  model=list(past_obs=c(1, 12)), xreg=regressors, distr="poisson")
summary(seatbeltsfit)