Classify and summarize missing values in a dataset
Routines classifies codes of missing valuesas numbers in objects of the compositions package.
missingSummary(x,..., vlabs = colnames(x),
mc=attr(x,"missingClassifier"),
values=eval(formals(missingType)$values))
missingType(x,..., mc=attr(x,"missingClassifier"),
values=c("NMV", "BDL", "MAR", "MNAR", "SZ", "Err"))x |
a dataset which might contain missings |
... |
additional arguments for mc |
mc |
optionally in missingSummary, an alternate routine to be used
instead of |
vlabs |
labels for the variables |
values |
the names of the different types of missings. |
The function mainly counts the various types of missing values.
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.
K. Gerald van den Boogaart
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
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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