Classify and summarize missing values in a dataset

Description

Routines classifies codes of missing valuesas numbers in objects of the compositions package.

Usage

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   missingSummary(x,..., vlabs = colnames(x), 
                 mc=attr(x,"missingClassifier"), 
                 values=eval(formals(missingType)$values))
   missingType(x,..., mc=attr(x,"missingClassifier"),
                 values=c("NMV", "BDT", "MAR", "MNAR", "SZ", "Err"))

Arguments

x

a dataset which might contain missings

...

additional arguments for mc

mc

optionally in missingSummary, an alternate routine to be used instead of missingType

vlabs

labels for the variables

values

the names of the different types of missings. "Err" is a value that can not be classified e.g. Inf.

Details

The function mainly counts the various types of missing values.

Value

missingType returns a character vector/matrix with the same dimension and dimnames as x giving the type of every value.
missingSummary returns a table giving the number of missings of each type for each variable.

Author(s)

K. Gerald van den Boogaart

References

Boogaart, K.G., R. Tolosana-Delgado, M. Bren (2006) Concepts for the handling of zeros and missings in compositional data, Proceedings of IAMG 2006, Liege

See Also

compositions.missing

Examples

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

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