Robust EMalgorithm ER
Description
The ER
function is an implementation of the ERalgorithm of Little and Smith (1987).
Usage
1 2 
Arguments
data 
a data frame or matrix 
weights 
sampling weights 
alpha 
probability for the quantile of the cutoff 
psi.par 
further parameters passed to the psifunction 
em.steps 
number of iteration steps of the EMalgorithm 
steps.output 
if 
Estep.output 
if 
tolerance 
convergence criterion (relative change) 
Details
The Mstep of the EMalgorithm uses a onestep Mestimator.
Value
sample.size 
number of observations 
number.of.variables 
Number of variables 
significance.level 

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, 5868.
See Also
BEM
Examples
1 2 3 4 
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.