Description Objects from the Class Slots Extends Details Accessor and mutator methods Methods UML class diagram Note Author(s) References See Also Examples
Class for controlling contamination in a simulation experiment. The values of the contaminated observations will be distributed completely at random (DCAR), i.e., they will not depend on on the original values.
Objects can be created by calls of the form
new("DCARContControl", ...)
, DCARContControl(...)
or
ContControl(..., type="DCAR")
(the latter exists mainly for back
compatibility with early draft versions of simFrame
).
target
:Object of class "OptCharacter"
; a character
vector specifying specifying the variables (columns) to be contaminated,
or NULL
to contaminate all variables (except the additional ones
generated internally).
epsilon
:Object of class "numeric"
giving the
contamination levels.
grouping
:Object of class "character"
specifying a
grouping variable (column) to be used for contaminating whole groups
rather than individual observations (the same values are used for all
observations in the same group).
aux
:Object of class "character"
specifying an
auxiliary variable (column) whose values are used as probability weights
for selecting the items (observations or groups) to be contaminated.
distribution
:Object of class "function"
generating
the values of the contamination data, e.g.,
rnorm
(the default) or rmvnorm
from
package mvtnorm. It should take a non-negative integer as its
first argument, giving the number of items to be created, and return an
object that can be coerced to a data.frame
, containing the
contamination data.
dots
:Object of class "list"
containing additional
arguments to be passed to distribution
.
Class "ContControl"
, directly.
Class "VirtualContControl"
, by class "ContControl", distance 2.
Class "OptContControl"
, by class "ContControl", distance 3.
With this control class, contamination is modeled as a two-step process. The
first step is to select observations to be contaminated, the second is to
model the distribution of the outliers. In this case, the values of the
contaminated observations will be generated by the function given by slot
fun
and will not depend on on the original values.
In addition to the accessor and mutator methods for the slots inherited from
"ContControl"
, the following are available:
getDistribution
signature(x = "DCARContControl")
: get
slot distribution
.
setDistribution
signature(x = "DCARContControl")
: set
slot distribution
.
getDots
signature(x = "DCARContControl")
: get slot
dots
.
setDots
signature(x = "DCARContControl")
: set slot
dots
.
Methods are inherited from "ContControl"
.
A slightly simplified UML class diagram of the framework can be found in
Figure 1 of the package vignette An Object-Oriented Framework for
Statistical Simulation: The R Package simFrame
. Use
vignette("simFrame-intro")
to view this vignette.
The slot grouping
was named group
prior to version 0.2.
Renaming the slot was necessary since accessor and mutator functions were
introduced in this version and a function named getGroup
already
exists.
Andreas Alfons
Alfons, A., Templ, M. and Filzmoser, P. (2010) An Object-Oriented Framework for Statistical Simulation: The R Package simFrame. Journal of Statistical Software, 37(3), 1–36. doi: 10.18637/jss.v037.i03.
Alfons, A., Templ, M. and Filzmoser, P. (2010) Contamination Models in the R Package simFrame for Statistical Simulation. In Aivazian, S., Filzmoser, P. and Kharin, Y. (editors) Computer Data Analysis and Modeling: Complex Stochastic Data and Systems, volume 2, 178–181. Minsk. ISBN 978-985-476-848-9.
Béguin, C. and Hulliger, B. (2008) The BACON-EEM Algorithm for Multivariate Outlier Detection in Incomplete Survey Data. Survey Methodology, 34(1), 91–103.
Hulliger, B. and Schoch, T. (2009) Robust Multivariate Imputation with Survey Data. 57th Session of the International Statistical Institute, Durban.
"DARContControl"
, "ContControl"
,
"VirtualContControl"
, contaminate
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