print: Printing compositional data.

Description Usage Arguments Details Value Missing Policy Author(s) References See Also Examples

Description

Prints compositional objects with appropriate missing encodings.

Usage

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## S3 method for class 'acomp'
print(x,...,replace0=TRUE)
## S3 method for class 'aplus'
print(x,...,replace0=TRUE)
## S3 method for class 'rcomp'
print(x,...,replace0=FALSE)
## S3 method for class 'rplus'
print(x,...,replace0=FALSE)

Arguments

x

a compositional object

...

further arguments to print.default

replace0

logical: Shall 0 be treated as "Below detection Limit" with unkown limit.

Details

Missings are displayed with an appropriate encoding:

  • MARMissing at random: The value is missing independently of its true value.

  • MNARMissing NOT at random: The value is missing dependently of its true value, but without a known systematic. Maybe a better name would be: Value dependen missingness.

  • BDTbelow detection limit (with unspecified detection limit): The value is missing because it was below an unkown detection limit.

  • <Detectionlimitbelow detection limit (with specified detection limit): The value is below the displayed detection limit.

  • SZStructural Zero: A true value is either bound to be zero or does not exist for structural nonrandom reasons. E.g. the portion of pregnant girls at a boys school.

  • ERRError: An illegal encoding value was found in the object.

Value

An invisible version of x.

Missing Policy

The policy of treatment of zeroes, missing values and values below detecion limit is explained in depth in compositions.missings.

Author(s)

K.Gerald v.d. Boogaart http://www.stat.boogaart.de, Raimon Tolosana-Delgado

References

Boogaart, K.G. v.d., R. Tolosana-Delgado, M. Bren (2006) Concepts for handling of zeros and missing values in compositional data, in: E. Pirard (ed.) (2006)Proceedings of the IAMG'2006 Annual Conference on "Quantitative Geology from multiple sources", September 2006, Liege, Belgium,, S07-01, 4pages, ISBN 978-2-9600644-0-7

See Also

clr,acomp, plot.acomp, boxplot.acomp, barplot.acomp, mean.acomp, var.acomp, variation.acomp, zeroreplace

Examples

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data(SimulatedAmounts)
mydata <- simulateMissings(sa.groups5,dl=0.01,knownlimit=TRUE,
                     MAR=0.05,MNARprob=0.05,SZprob=0.05)
mydata[1,1]<-BDLvalue
print(aplus(mydata))
print(aplus(mydata),digits=3)
print(acomp(mydata))
print(rplus(mydata))
print(rcomp(mydata))


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