compositions (version 1.01-1)

simulatemissings: Artifical simulation of various kinds of missings

Description

This is a simulation mechanism to check the missing technique. It just generates additional missings of the various types in a given dataset.

Usage

simulateMissings(x,detectionlimit=NULL,
     knownlimit=FALSE,MARprob=0.0,MNARprob=0.0,
     mnarity=0.5,SZprob=0.0)

Arguments

x

a dataset that should get the missings

detectionlimit

the detection limit described in clo, to impose an artificial detection limit

knownlimit

a boolean indicating wether the actual detection limit is still known in the dataset.

MARprob

the probability of occurence of 'Missings At Random' values

MNARprob

the probability of occurrence of 'Missings Not At Random'. The tendency is that small values have a higher probability to be missed.

mnarity

a number between 0 and 1 giving the strength of the influence of the actual value in becoming a MNAR. 0 means a MAR like behavior and 1 means that it is just the smallest values that is lost

SZprob

the probability to obtain a structural zero. This is done at random like a MAR.

Value

• A dataset like x but with some additional missings.

Details

Without any additional parameters no missings are generated. The procedure to generate MNAR affects all variables.

References

See compositions.missings for more details.

See Also

compositions.missings

Examples

data(SimulatedAmounts)
x <- acomp(sa.lognormals)
xnew <- simulateMissings(x,detectionlimit=0.05,MAR=0.05,MNAR=0.05,SZ=0.05)
acomp(xnew)
plot(missingSummary(xnew))