ER: Robust EM-algorithm ER
In modi: Multivariate outlier detection and imputation for incomplete survey data

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

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

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

`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)

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

`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

Beat Hulliger

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

BEM

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))