# summary.tsglm

0th

Percentile

##### Summarising Fits of Count Time Series following Generalised Linear Models

summary method for class "tsglm".

Keywords
inference, Model assessment
##### Usage
"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.
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.

S3 method print.

tsglm for fitting a GLM for time series of counts.

##### Aliases
• summary.tsglm
• print.summary.tsglm
##### 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: