After running EAdet
an imputation of the detected outliers with EAimp
may be run.
1 2 3 4 5 6 7 
data 
a data frame or matrix with the data 
weights 
a vector of positive sampling weights 
outind 
a logical vecotr with component TRUE for outliers 
reach 
reach of the threshold function (usually set to the maximum distance to a nearest neighbour, see internal function 
transmission.function 
form of the transmission function of distance 
power 
sets 
distance.type 
distance type in function 
maxl 
Maximum number of steps without infection 
monitor 
if 
threshold 
Infect all remaining points with infection probability above the threshold 
deterministic 
if 
duration 
The duration of the detection epidemic 
kdon 
The number of donors that should be infected before imputation 
fixedprop 
If 
EAimp
uses the distances calculated in EAdet
(actually the counterprobabilities, which are stored in a global data set) and starts an epidemic at each observation to be imputed until donors for the missing values are infected. Then a donor is selected randomly.
EAimp
returns a list with components parameters
and imputed.data
.
parameters
contains the following components:
sample.size 
Number of observations 
number.of.variables 
Number of variables 
n.complete.records 
Number of records without missing values 
n.usable.records 
Number of records with less than half of values missing (unusable observations are discarded) 
duration 
Duration of epidemic 
reach 
Transmission distance (d0) 
threshold 
Input parameter 
deterministic 
Input parameter 
computation.time 
Elapsed computation time 
imputed.data
contains the imputed data.
Beat Hulliger
B\'eguin, C., and Hulliger, B. (2004). Multivariate oulier detection in incomplete survey data: The epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society, A 167(Part 2.), 275294.
EAdet
for outlier detection with the Epicemic Algorithm.
1 2 3 4 5  data(bushfirem,bushfire.weights)
det.res<EAdet(bushfirem,bushfire.weights)
imp.res<EAimp(bushfirem,bushfire.weights,outind=det.res$outind,
reach=det.res$output$max.min.di,kdon=3)
print(imp.res$output)

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