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 Classical and robust regression of non-compositional response on compositional predictors
Delivers appropriate inference for regression of y on a compositional matrix X.
lmCoDaX(y, X, method = "robust")
ilrregression(X, y)
robilrregression(X, y)
The response which should be non-compositional
The compositional predictors as a matrix, data.frame or numeric vector
If robust, LTS-regression is applied, while with method equals \dQuote{classical},
the conventional least squares regression is applied.
Compositional explanatory variables should not be directly used in a linear regression model
because any inference statistic can become misleading. While various approaches for this
problem were proposed, here an approach based on the isometric logratio (ilr) transformation
is used. 
An object of class \sQuote{lts} or \sQuote{lm} and two summary objects.
 Filzmoser, P., Hron, K., Thompsonc, K. (2012)
Linear regression with compositional explanatory variables. 
\emph{Journal of Applied Statistics}, 39, 1115-1128.
Peter Filzmoser
## How the total household expenditures in EU Member
## States depend on relative contributions of 
## single household expenditures:
y <- as.numeric(apply(expendituresEU,1,sum))
lmCoDaX(y, expendituresEU, method="classical")
lmCoDaX(y, expendituresEU, method="robust")
\keyword{ models }
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