EAimp: Epidemic Algorithm for imputation of multivariate outliers in...
In modi: Multivariate outlier detection and imputation for incomplete survey data

Description Usage Arguments Details Value Author(s) References See Also Examples

After running EAdet an imputation of the detected outliers with EAimp may be run.

EAimp(data, weights ,  outind, reach="max",      transmission.function = "root", 

power=ncol(data), distance.type = "euclidean", 

duration = 5, maxl = 5,
kdon = 1, monitor = FALSE, threshold = FALSE,
deterministic = TRUE, fixedprop = 0)

`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 `.EA.dist`)
`transmission.function`	form of the transmission function of distance `d`: `"step"` is a heaviside function which jumps to `1` at `d0`, `"linear"` is linear between `0` and `d0`, `"power"` is `(beta*d+1)^(-p)` for `p=ncol(data)` as default, `"root"` is the function `1-(1-d/d0)^(1/maxl)`
`power`	sets `p=power`, where `p` is the parameter in the above transmission function.
`distance.type`	distance type in function `dist()`
`maxl`	Maximum number of steps without infection
`monitor`	if `TRUE` verbose output on epidemic
`threshold`	Infect all remaining points with infection probability above the threshold `1-0.5^(1/maxl)`
`deterministic`	if `TRUE` the number of infections is the expected number and the infected observations are the ones with largest infection probabilities.
`duration`	The duration of the detection epidemic
`kdon`	The number of donors that should be infected before imputation
`fixedprop`	If `TRUE` a fixed proportion of observations is infected at each step

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.), 275-294.

EAdet for outlier detection with the Epicemic Algorithm.

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)