View source: R/zerotreatment.R
missingProjector | R Documentation |
Returns projectors on the observed subspace in the presence of missings.
missingProjector(x,...,by="s")
## S3 method for class 'acomp'
missingProjector(x,has=is.NMV(x),...,by="s")
## S3 method for class 'aplus'
missingProjector(x,has=is.NMV(x),...,by="s")
## S3 method for class 'rcomp'
missingProjector(x,has=!(is.MAR(x)|is.MNAR(x)),...,by="s")
## S3 method for class 'rplus'
missingProjector(x,has=!(is.MAR(x)|is.MNAR(x)),...,by="s")
x |
a dataset or object of the given class |
has |
a boolean matrix of the same size indicating nonmissing values |
... |
additional arguments for generic purpose only |
by |
the name of the dataset dimension on |
See the references for details on that function.
A dataset 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.
The function sumMissingProjector
takes all these matrices
and sums
them, generating a "summary" of observed sub-spaces. 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).
K.G.van den Boogaart
Boogaart, K.G. v.d. (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
missingsInCompositions
data(SimulatedAmounts)
x <- acomp(sa.lognormals)
xnew <- simulateMissings(x,dl=0.05,MAR=0.05,MNAR=0.05,SZ=0.05)
xnew
plot(missingSummary(xnew))
missingProjector(acomp(xnew))
missingProjector(rcomp(xnew))
missingProjector(aplus(xnew))
missingProjector(rplus(xnew))
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