VIM (version 3.0.2)

irmi: Iterative robust model-based imputation (IRMI)

Description

In each step of the iteration, one variable is used as a response variable and the remaining variables serve as the regressors.

Usage

irmi(x, eps = 5, maxit = 100, mixed = NULL, count = NULL, step = FALSE, 
    robust = FALSE, takeAll = TRUE, noise = TRUE, noise.factor = 1,
    force = FALSE, robMethod = "MM", force.mixed = TRUE, mi = 1,
    addMixedFactors = FALSE, trace = FALSE,init.method="kNN")

Arguments

x

data.frame or matrix

eps

threshold for convergency

maxit

maximum number of iterations

mixed

column index of the semi-continuous variables

count

column index of count variables

step

a stepwise model selection is applied when the parameter is set to TRUE

robust

if TRUE, robust regression methods will be applied

takeAll

takes information of (initialised) missings in the response as well for regression imputation.

noise

irmi has the option to add a random error term to the imputed values, this creates the possibility for multiple imputation. The error term has mean 0 and variance corresponding to the variance of the regression residuals.

noise.factor

amount of noise.

force

if TRUE, the algorithm tries to find a solution in any case, possible by using different robust methods automatically.

robMethod

regression method when the response is continuous.

force.mixed

if TRUE, the algorithm tries to find a solution in any case, possible by using different robust methods automatically.

addMixedFactors

if factor variables for the mixed variables should be created for the regression models

mi

number of multiple imputations.

trace

Additional information about the iterations when trace equals TRUE.

init.method

Method for initialization of missing values (kNN or median)

Value

• the imputed data set.

Details

The method works sequentially and iterative. The method can deal with a mixture of continuous, semi-continuous, ordinal and nominal variables including outliers.

A full description of the method will be uploaded soon in form of a package vignette.

References

M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwise regression imputation using standard and robust methods. Journal of Computational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.

See Also

mi

Examples

data(sleep)
irmi(sleep)