algo.bayes {surveillance} | R Documentation |
Evaluation of timepoints with the Bayes subsystem 1,2 or 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 shoud be evaluated in algo.rkiLatestTimepoint . The
default is to use the latest timepoint |
control |
control object: range determines the desired
timepoints which should be evaluated, b describes the number of years to go
back for the reference values, w is the half window width for the reference
values around the appropriate timepoint and actY is a boolean to decide if
the year of timePoint also spend w reference values of the past. The parameter alpha is the 1-α-quantile to use in order to calculate the upper threshold.
As default b , w , actY are set for the Bayes 1 system with alpha =0.05.
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Using the reference values for calculating an upper limit (threshold) via the negative
binomial distribution, alarm is given if the actual value is bigger or equal than this threshold.
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 ago and alpha=0.05 fixed.
"Bayes 2"
reference values from 6 weeks ago and
13 weeks of the year ago (symmetrical around the
comparable week). 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 |
algo.bayesLatestTimepoint returns a list of class survRes (surveillance result), which
includes the alarm value for recognizing an
outbreak (1 for alarm, 0 for no alarm), the threshold value for recognizing the alarm and
the input object of class disProg.
algo.bayes gives a list of class survRes which includes the vector
of alarm values for every timepoint in range and the vector of threshold values
for every timepoint in range for the system specified by b , w and
actY , the range and the input object of class disProg.
algo.bayes1 returns the same for the Bayes 1 system, algo.bayes2
for the Bayes 2 system and algo.bayes3 for the Bayes 3 system.
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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))