interv_test.tsglm
Testing for Interventions in Count Time Series Following Generalised Linear Models
Test for one or more interventions of given type at given time as proposed by Fokianos and Fried (2010, 2012).
 Keywords
 Intervention detection
Usage
"interv_test"(fit, tau, delta, external, info=c("score"), est_interv=FALSE, ...)
Arguments
 fit

an object of class
"tsglm"
. Usually the result of a call totsglm
.  tau
 integer vector of times at which the interventions occur which are tested for.
 delta

numeric vector that determines the types of the interventions (see Details). Must be of the same length as
tau
.  external

logical vector of length
length(tau)
specifying for each intervention wether its effect is external or not (see Details). If this is only a scalar this choice will be used for all interventions. If this is only a scalar this choice will be used for all interventions. If omitted all interventions will have an internal effect (i.e.external=FALSE
).  info

character value that determines how to calculate the information matrix, see
tsglm
. Currently"score"
is the only possible choice.  est_interv

logical value. If
est_interv=TRUE
a fit for the model with all specified interventions is computed and additionally returned.  ...

additional arguments passed to the fitting function
tsglm
.
Details
A score test on the null hypothesis of no interventions is done. The null hypothesis is that the data are generated from the model specified in the argument model
, see definition in tsglm
. Under the alternative there are one or more intervention effects occuring at times tau
. The types of the intervention effects are specified by delta
as defined in interv_covariate
. The interventions are included as additional covariates according to the definition in tsglm
. It can have an internal (the default) or external (external=TRUE
) effect (see Liboschik et al., 2014).
Under the null hypothesis the test statistic has asymptotically a chisquare distribution with length(tau)
(i.e. the number of breaks) degrees of freedom. The returned pvalue is based on this and approximately valid for long time series, i.e. when length(ts)
large.
Value

An object of class
 test_statistic
 value of the test statistic.
 df
 degrees of freedom of the chisquared distribution the test statistic is compared with.
 p_value
 pvalue of the test.
 fit_H0

object of class
"tsglm"
with the fitted model under the null hypothesis of no intervention, seetsglm
.  model_interv
 model specification of the model with the specified interventions. If argument
 fit_interv

object of class
"tsglm"
with the fitted model with the specified interventions, seetsglm
.
"interv_test"
, which is a list with at least the following components:est_interv=TRUE
, the following component is additionally returned:References
Fokianos, K. and Fried, R. (2010) Interventions in INGARCH processes. Journal of Time Series Analysis 31(3), 210225, http://dx.doi.org/10.1111/j.14679892.2010.00657.x.
Fokianos, K., and Fried, R. (2012) Interventions in loglinear Poisson autoregression. Statistical Modelling 12(4), 299322. http://dx.doi.org/10.1177/1471082X1201200401.
Liboschik, T., Kerschke, P., Fokianos, K. and Fried, R. (2014) Modelling interventions in INGARCH processes. International Journal of Computer Mathematics (published online), http://dx.doi.org/10.1080/00207160.2014.949250.
See Also
S3 method print
.
tsglm
for fitting a GLM for time series of counts.
interv_detect
for detection of single interventions of given type and interv_multiple
for iterative detection of multiple interventions of unknown types. interv_covariate
for generation of deterministic covariates describing intervention effects.
Examples
###Campylobacter infections in Canada (see help("campy"))
#Test for the intervention effects which were found in Fokianos und Fried (2010):
campyfit < tsglm(ts=campy, model=list(past_obs=1, past_mean=c(7,13)))
campyfit_intervtest < interv_test(fit=campyfit, tau=c(84,100), delta=c(1,0))
campyfit_intervtest