print.acomp {compositions} | R Documentation |
Printing compositional data.
Description
Prints compositional objects with appropriate missing encodings.
Usage
## 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 |
replace0 |
logical: Shall 0 be treated as "Below detection Limit" with unkown limit. |
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.
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
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))