# algo.quality: Computation of Quality Values for a Surveillance System...

### Description

Computation of the quality values for a surveillance System output.

### Usage

 1 algo.quality(sts, penalty = 20)

### Arguments

 sts object of class survRes or sts, which includes the state chain and the computed alarm chain penalty the maximal penalty for the lag

### Details

The lag is defined as follows: In the state chain just the beginnings of an outbreak chain (outbreaks directly following each other) are considered. In the alarm chain, the range from the beginning of an outbreak until min(next outbreak beginning,\code{penalty}) timepoints is considered. The penalty timepoints were chosen, to provide an upper bound on the penalty for not discovering an outbreak. Now the difference between the first alarm by the system and the defined beginning is denoted “the lag” Additionally outbreaks found by the system are not punished. At the end, the mean of the lags for every outbreak chain is returned as summary lag.

### Value

 list of quality values TP: Number of correct found outbreaks. FP: Number of false found outbreaks. TN: Number of correct found non outbreaks. FN: Number of false found non outbreaks. sens: True positive rate, meaning TP/(FN + TP). spec: True negative rate, meaning TN/(TN + FP). dist: Euclidean distance between (1-spec, sens) to (0,1). lag: Lag of the outbreak recognizing by the system.

algo.compare

### Examples

 1 2 3 4 5 6 7 8 9 10 # Create a test object disProgObj <- sim.pointSource(p = 0.99, r = 0.5, length = 200, A = 1, alpha = 1, beta = 0, phi = 0, frequency = 1, state = NULL, K = 1.7) # Let this object be tested from rki1 survResObj <- algo.rki1(disProgObj, control = list(range = 50:200)) # Compute the quality values algo.quality(survResObj)

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