winsimp: Winsorization followed by imputation
In modi: Multivariate outlier detection and imputation for incomplete survey data

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

Winsorisation of outliers according to the Mahalanobis distance followed by an imputation under the multivariate normal model. Only the outliers are winsorized. The Mahalanobis distance MDmiss allows for missing values.

1	Winsimp(data, center, scatter, outind, seed = 1000003)

`data`	Data frame with the data
`center`	(Robust) estimate of the center (location) of the observations
`scatter`	(Robust) estimate of the scatter (covariance-matrix) of the observations
`outind`	Logical vector indicating outliers with 1 or TRUE for outliers
`seed`	Seed for random number generator

It is assumed that center, scatter and outind stem from a multivariate outlier detection algorithm which produces robust estimates and which declares outliers observations with a large Mahalanobis distance. The cutpoint is calculated as the least (unsquared) Mahalanobis distance among the outliers. The winsorization reduces the weight of the outliers:

y_i=μ_R +(y_i-μ_R)*c/d_i

, where μ_R is the robust center and d_i is the (unsquared) Mahalanobis distance of observation i.

Function winsimp returns a list whose first component output is a sub-list with the follwing components:

`cutpoint`	Cutpoint for outliers
`proc.time`	Processing time
`n.missing.before`	Number of missing values before
`n.missing.after`	Number of missing values after imputation

The further component returned by winsimp is

imputed.data

Imputed data set.

Beat Hulliger

Hulliger, B. (2007) Multivariate Outlier Detection and Treatment in Business Surveys, Proceedings of the III International Conference on Establishment Surveys, Montr\'eal.

MDmiss. Uses imp.norm from the norm package.

data(bushfirem,bushfire.weights)
det.res<-TRC(bushfirem,weight=bushfire.weights)
imp.res<-Winsimp(bushfirem,det.res$output$center,det.res$output$scatter,det.res$outind)
print(imp.res$output)