modi (version 0.1.2)

ER: Robust EM-algorithm ER

Description

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

Usage

ER(
  data,
  weights,
  alpha = 0.01,
  psi.par = c(2, 1.25),
  em.steps = 100,
  steps.output = FALSE,
  Estep.output = FALSE,
  tolerance = 1e-06
)

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

Arguments

data

a data frame or matrix with the data.

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).

Author

Beat Hulliger

Details

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

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

data(bushfirem, 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))