simulatemissings {compositions} | R Documentation |
This is a simulation mechanism to check the missing technique. It just generates additional missings of the various types in a given dataset.
simulateMissings(x,detectionlimit=NULL, knownlimit=FALSE,MARprob=0.0,MNARprob=0.0, mnarity=0.5,SZprob=0.0)
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. |
Without any additional parameters no missings are generated. The procedure to generate MNAR affects all variables.
A dataset like x
but with some additional missings.
K.Gerald van den Boogaart
See compositions.missings for more details.
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))