A class providing the means to analyse count compositions understood as Poisson or multinomial realisation, where the portions are given by an unkown Aitchison compositions.
ccomp(X,parts=1:NCOL(oneOrDataset(X)),total=NA,warn.na=FALSE,
detectionlimit=NULL,BDL=NULL,MAR=NULL,MNAR=NULL,SZ=NULL)
composition or dataset of compositions
vector containing the indices xor names of the columns to be used
the total amount to be used, typically 1 or 100
should the user be warned in case of NA,NaN or 0 coding different types of missing values?
a number, vector or matrix of positive numbers giving the detection limit of all values, all columns or each value, respectively
the code for 'Below Detection Limit' in X
the code for 'Structural Zero' in X
the code for 'Missing At Random' in X
the code for 'Missing Not At Random' in X
a vector of class "ccomp"
representing count composition
or a matrix of class "ccomp"
representing
multiple count compositions each in one row.
The policy of treatment of zeroes, missing values and values below detecion limit is explained in depth in compositions.missing.
A count composition contains an indirect observation of an Aitchison composition by a Poisson or multinomial variable. A count composition can only contain integer counts. It is assumed that the total sum is a an artefact and does not contain information on the actual composition. But it does contain information on the precision of the relative observation.
barplot.ccomp
ccompMultinomialGOF.test
ccompPoissonGOF.test
cdt.ccomp
cdtInv.ccomp
fitSameMeanDifferentVarianceModel
groupparts.ccomp
idt.ccomp
idtInv.ccomp
is.ccomp
mean.ccomp
names<-.ccomp
names.ccomp
plot.ccomp
PoissonGOF.test
rmultinom.ccomp
rnorm.ccomp
rpois.ccomp
split.ccomp
totals.ccomp
# NOT RUN {
data(SimulatedAmounts)
plot(acomp(sa.lognormals))
# }
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