Robust EM-algorithm ER

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Description

The ER function is an implementation of the ER-algorithm of Little and Smith (1987).

Usage

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ER(data, weights, alpha = 0.01, psi.par = c(2, 1.25), 
em.steps = 100, steps.output = FALSE, Estep.output=FALSE, tolerance=1e-6)

Arguments

data

a data frame or matrix

weights

sampling weights

alpha

probability for the quantile of the cut-off

psi.par

further parameters passed to the psi-function

em.steps

number of iteration steps of the EM-algorithm

steps.output

if TRUE verbose output

Estep.output

if TRUE estimators are output at each iteration

tolerance

convergence criterion (relative change)

Details

The M-step of the EM-algorithm uses a one-step M-estimator.

Value

sample.size

number of observations

number.of.variables

Number of variables

significance.level

alpha

computation.time

Elapsed computation time

good.data

Indices of the data in the final good subset

outliers

Indices of the outliers

center

Final estimate of the center

scatter

Final estimate of the covariance matrix

dist

Final Mahalanobis distances

rob.weights

Robustness weights in the final EM step

Author(s)

Beat Hulliger

References

Little, R. and P. Smith (1987). Editing and imputation for quantitative survey data. Journal of the American Statistical Association, 82, 58-68.

See Also

BEM

Examples

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data(bushfirem)
data(bushfire.weights)
det.res<-ER(bushfirem, weights=bushfire.weights,alpha=0.05,steps.output=TRUE,em.steps=100,tol=2e-6)
PlotMD(det.res$dist,ncol(bushfirem))