algo.bayes  R Documentation 
Evaluation of timepoints with the Bayes subsystem 1, 2, 3 or a self defined Bayes subsystem.
algo.bayesLatestTimepoint(disProgObj, timePoint = NULL,
control = list(b = 0, w = 6, actY = TRUE,alpha=0.05))
algo.bayes(disProgObj, control = list(range = range,
b = 0, w = 6, actY = TRUE,alpha=0.05))
algo.bayes1(disProgObj, control = list(range = range))
algo.bayes2(disProgObj, control = list(range = range))
algo.bayes3(disProgObj, control = list(range = range))
disProgObj 
object of class disProg (including the observed and the state chain) 
timePoint 
time point which should be evaluated in

control 
control object: 
Using the reference values the (1\alpha)\cdot
100\%
quantile of the
predictive posterior distribution is calculated as a threshold.
An alarm is given if the actual value is bigger or equal than this threshold.
It is possible to show using analytical computations that the predictive
posterior in this case is the negative
binomial distribution. Note: algo.rki
or algo.farrington
use twosided prediction intervals – if one wants to compare with
these procedures it is necessary to use an alpha, which is half the
one used for these procedures.
Note also that algo.bayes
calls
algo.bayesLatestTimepoint
for the values specified in
range
and for the system specified in control
.
algo.bayes1
, algo.bayes2
, algo.bayes3
call
algo.bayesLatestTimepoint
for the values specified in
range
for the Bayes 1 system, Bayes 2 system or Bayes 3 system.
"Bayes 1"
reference values from 6 weeks. Alpha is fixed a
t 0.05.
"Bayes 2"
reference values from 6 weeks ago and
13 weeks of the previous year (symmetrical around the
same week as the current one in the previous year). Alpha is fixed at 0.05.
"Bayes 3"
18 reference values. 9 from the year ago
and 9 from two years ago (also symmetrical around the
comparable week). Alpha is fixed at 0.05.
The procedure is now able to handle NA
's in the reference
values. In the summation and when counting the number of observed
reference values these are simply not counted.
survRes 

M. Höhle, A. Riebler, C. Lang
Riebler, A. (2004), Empirischer Vergleich von statistischen Methoden zur Ausbruchserkennung bei Surveillance Daten, Bachelor's thesis.
algo.call
, algo.rkiLatestTimepoint
and algo.rki
for
the RKI system.
disProg < sim.pointSource(p = 0.99, r = 0.5, length = 208, A = 1,
alpha = 1, beta = 0, phi = 0,
frequency = 1, state = NULL, K = 1.7)
# Test for bayes 1 the latest timepoint
algo.bayesLatestTimepoint(disProg)
# Test week 200 to 208 for outbreaks with a selfdefined bayes
algo.bayes(disProg, control = list(range = 200:208, b = 1,
w = 5, actY = TRUE,alpha=0.05))
# The same for bayes 1 to bayes 3
algo.bayes1(disProg, control = list(range = 200:208,alpha=0.05))
algo.bayes2(disProg, control = list(range = 200:208,alpha=0.05))
algo.bayes3(disProg, control = list(range = 200:208,alpha=0.05))
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