missingsummary {compositions} | R Documentation

## Classify and summarize missing values in a dataset

### Description

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

### Usage

       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

### 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)

### 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

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

[Package compositions version 0.91-6 Index]