ER: Robust EM-algorithm ER

In modi: Multivariate Outlier Detection and Imputation for Incomplete Survey Data

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
)

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).

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

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))

modi documentation built on March 31, 2023, 8:35 p.m.