compositions (version 2.0-8)

print.acomp: Printing compositional data.

Description

Prints compositional objects with appropriate missing encodings.

Usage

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

Value

An invisible version of x.

Arguments

x

a compositional object

...

further arguments to print.default

replace0

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

Missing Policy

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

Author

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

Details

Missings are displayed with an appropriate encoding:

MAR

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

MNAR

Missing 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.

BDL

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

<Detectionlimit

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

SZ

Structural 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.

ERR

Error: An illegal encoding value was found in the object.

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

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))