Routines to compute the global projector to the observed subspace, down-weighting the subspaces with more missing values.
sumMissingProjector(x,...)
# S3 method for acomp
sumMissingProjector(x,has=is.NMV(x),...)
# S3 method for aplus
sumMissingProjector(x,has=is.NMV(x),...)
# S3 method for rcomp
sumMissingProjector(x,has=!(is.MAR(x)|is.MNAR(x)),...)
# S3 method for rplus
sumMissingProjector(x,has=!(is.MAR(x)|is.MNAR(x)),...)
# S3 method for rmult
sumMissingProjector(x,has=is.finite(x),...)
The matrix of rotation/re-weighting of the original data set, down-weighting the subspaces with more missing values. This matrix is useful to obtain estimates of the mean (and variance, in the future) still unbiased in the presence of lost values (only of type MAR, stricly-speaking, but anyway useful for any type of missing value, when used with care). This matrix is the Fisher Information in the presence of missing values.
a dataset of some type containing missings
the values to be regarded as non missing
further generic arguments that might be useful for other functions.
No missing policy is given by the routine itself. Its treatment of missing values depends on the "has" argument.
K.Gerald v.d. Boogaart http://www.stat.boogaart.de, Raimon Tolosana-Delgado
The function missingProjector
generates a list of N square
matrices of dimension DxD (with N and D respectively
equal to the number of rows and columns in x
). Each of these
matrices gives the projection of a data row onto its observed sub-space.
Then, the function sumMissingProjector
takes all these matrices and
sums them in a efficient way, generating a "summary" of observed sub-spaces.
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)Proccedings of the IAMG'2006 Annual Conference on "Quantitative Geology from multiple sources", September 2006, Liege, Belgium, S07-01, 4pages, http://stat.boogaart.de/Publications/iamg06_s07_01.pdf
missingProjector
,
clr
,rcomp
, aplus
,
princomp.acomp
,
plot.acomp
, boxplot.acomp
,
barplot.acomp
, mean.acomp
,
var.acomp
, variation.acomp
,
cov.acomp
, msd
data(SimulatedAmounts)
sumMissingProjector(acomp(sa.lognormals))
sumMissingProjector(acomp(sa.tnormals))
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