"interv_multiple"(fit, taus=2:length(fit$ts), deltas=c(0,0.8,1), external=FALSE, B=10, signif_level=0.05, start.control_bootstrap, final.control_bootstrap, inter.control_bootstrap, parallel=FALSE, ...)
"tsglm"
. Usually the result of a call to tsglm
.
0 <= signif_level="" <="1
giving a significance level for the procedure.
=>start.control
in tsglm
. If missing, the same settings as for the regular estimation are used.
final.control
in tsglm
. If missing, the same settings as for the regular estimation are used. If final.control_bootstrap=NULL
, then the model is not re-fitted for each bootstrap sample. Instead the parameters of the original fit which have been used for simulating the bootstrap samples are used. This approach saves computation time at the cost of a more conservative procedure, see Fokianos and Fried (2012).
inter.control
in tsglm
. If missing, the same settings as for the regular estimation are used.
parallel=TRUE
, the bootstrap is distributed to multiple cores parallely. Requires a computing cluster to be initialised and registered as the default cluster by makeCluster
and setDefaultCluster
from package parallel
.
interv_detect
and via this function some of the arguments are passed to the fitting function tsglm
.
"interv_multiple"
, which is a list with the following components:tau
, delta
, size
, test_statistic
and p-value
.
"tsglm"
with the fitted model under the null hypothesis of no intervention, see tsglm
.
"tsglm"
with the fitted model for the cleanded time series after the last step of the iterative procedure, see tsglm
.
"tsglm"
with the fitted model with all detected interventions at their respective times, see tsglm
.
tau_max
gives the times where the test statistic has its maximum for each type of intervention and in each iteration step and element size
gives the estimated sizes of the respective intervention effects. Elements test_statistic
and p_value
require no further explanation.
interv_detect
is applied for each of the possible intervention types provided in the argument deltas
. If there is (after a Bonferroni correction) no significant intervention effect the procedure stops. Otherwise the type of intervention with the minimum p-value is chosen. In case of equal p-values preference is given to a level shift (i.e. $\delta=1$) and then to the type of intervention with the largest test statistic. The effect of the chosen intervention is removed from the time series. The time series cleaned from the intervention effect is tested for further interventions in a next step.For each time in taus
the test statistic of a score test on an intervention effect occuring at that time is computed, see interv_test
. The time with the maximum test statistic is considered as a candidate for a possible intervention effect at that time. The type of the intervention effect is specified by delta
as described in interv_covariate
. The intervention is included as an additional covariate according to the definition in tsglm
. It can have an internal (the default) or external (external=TRUE
) effect (see Liboschik et al., 2014).
All p-values given in the output are multiplied by the number of intervention types considered to account for the multiple testing in each step by a Bonferroni correction. Note that this correction can lead to p-values greater than one.
Note that this bootstrap procedure is very time-consuming.
Fokianos, K., and Fried, R. (2012) Interventions in log-linear Poisson autoregression. Statistical Modelling 12(4), 299--322. http://dx.doi.org/10.1177/1471082X1201200401.
Liboschik, T., Kerschke, P., Fokianos, K. and Fried, R. (2013) Modelling interventions in INGARCH processes. SFB 823 Discussion Paper 03/13, http://hdl.handle.net/2003/29878.
print
and plot
.tsglm
for fitting a GLM for time series of counts.
interv_test
for testing for intervention effects and interv_detect
for detection of single interventions of given type. interv_covariate
for generation of deterministic covariates describing intervention effects.
## Not run:
# ###Campylobacter infections in Canada (see help("campy"))
# #Searching for potential intervention effects (runs several hours!):
# campyfit <- tsglm(ts=campy, model=list(past_obs=1, past_mean=c(7,13)))
# campyfit_intervmultiple <- interv_multiple(fit=campyfit, taus=80:120,
# deltas=c(0,0.8,1), B=500, signif_level=0.05)
# campyfit_intervmultiple
# plot(campyfir_intervmultiple)
# #Parallel computation for shorter run time on a cluster:
# library(parallel)
# ntasks <- 3
# clust <- makeCluster(ntasks)
# setDefaultCluster(cl=clust)
# interv_multiple(fit=campyfit, taus=80:120, deltas=c(0,0.8,1), B=500,
# signif_level=0.05, parallel=TRUE)## End(Not run)
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