summary.tsglm
From tscount v1.3.0
by Tobias Liboschik
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 totsglm
.  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 loglikelihood 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
.
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)
Community examples
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