ccomp {compositions}

Count compositions

Description

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.

Usage

    ccomp(X,parts=1:NCOL(oneOrDataset(X)),total=NA,warn.na=FALSE,
            detectionlimit=NULL,BDL=NULL,MAR=NULL,MNAR=NULL,SZ=NULL)
          

Arguments

X

composition or dataset of compositions

parts

vector containing the indices xor names of the columns to be used

total

the total amount to be used, typically 1 or 100

warn.na

should the user be warned in case of NA,NaN or 0 coding different types of missing values?

detectionlimit

a number, vector or matrix of positive numbers giving the detection limit of all values, all columns or each value, respectively

BDL

the code for 'Below Detection Limit' in X

SZ

the code for 'Structural Zero' in X

MAR

the code for 'Missing At Random' in X

MNAR

the code for 'Missing Not At Random' in X

Details

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.

Value

a vector of class "ccomp" representing count composition or a matrix of class "ccomp" representing multiple count compositions each in one row.

Missing Policy

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

Author(s)

K.Gerald v.d. Boogaart http://www.stat.boogaart.de

See Also

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

Examples

data(SimulatedAmounts)
plot(acomp(sa.lognormals))