summary
method for class "tsglm"
.
1 2 
object 
an object of class 
B 
controls the computation of standard errors. Is passed to 
parallel 
controls the computation of standard errors. Is passed to 
level 
controls the computation of conficence intervals. Is passed to 
... 
further arguments are currently ignored. Only for compatibility with generic function. 
Computes and returns a list of summary statistics of the fitted model given in argument object
.
A named list with the following elements:
call 
see 
link 
see 
distr 
see 
residuals 
see 
coefficients 
data frame with estimated parameters, their standard errors and confidence intervals (based on a normal approximation or a parametric bootstrap, see 
level 
numerical value giving the coverage rate of the confidence intervals. 
number.coef 
number of coefficients. 
se.type 
type of standard errors, see 
se.bootstrapsamples 
number of bootstrap samples used for estimation of the standard errors, see 
logLik 
value of the loglikelihood function evaluated at the (quasi) maximum likelihood estimate. 
AIC 
Akaike's information criterion (AIC), see 
BIC 
Bayesian information criterion (BIC), see 
QIC 
Quasi information criterion (QIC), see 
pearson.resid 
Pearson residuals, see 
Tobias Liboschik and Philipp Probst
S3 method print
.
tsglm
for fitting a GLM for time series of counts.
1 2 3 4 5 6 7 8  ###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)

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