summary.tsglm: Summarising Fits of Count Time Series following Generalised...

In tscount: Analysis of Count Time Series

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)

Example output

Call:
tsglm(ts = timeseries, model = list(past_obs = c(1, 12)), xreg = regressors, 
    link = "log", distr = "poisson")

Coefficients:
             Estimate  Std.Error  CI(lower)  CI(upper)
(Intercept)    1.9129    0.34822    1.23038      2.595
beta_1         0.1039    0.07418   -0.04147      0.249
beta_12        0.1533    0.07745    0.00145      0.305
PetrolPrice   -0.1105    2.28012   -4.57945      4.358
linearTrend   -0.0384    0.00734   -0.05280     -0.024
Standard errors and confidence intervals (level =  95 %) obtained
by normal approximation.

Link function: log 
Distribution family: poisson 
Number of coefficients: 5 
Log-likelihood: -478.1634 
AIC: 966.3269 
BIC: 982.6143 
QIC: 966.3269 

tscount documentation built on Sept. 8, 2020, 9:08 a.m.