Classical and robust regression of non-compositional (real) response on compositional predictors
Delivers appropriate inference for regression of y on a compositional matrix X.
lmCoDaX(y, X, method = "robust")
y |
The response which should be non-compositional |
X |
The compositional predictors as a matrix, data.frame or numeric vector |
method |
If robust, LTS-regression is applied, while with method equals “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 pivot coordinates is used.
An object of class ‘lts’ or ‘lm’ and two summary objects.
Peter Filzmoser
Filzmoser, P., Hron, K., Thompsonc, K. (2012) Linear regression with compositional explanatory variables. Journal of Applied Statistics, 39, 1115-1128.
## How the total household expenditures in EU Member ## States depend on relative contributions of ## single household expenditures: data(expendituresEU) y <- as.numeric(apply(expendituresEU,1,sum)) lmCoDaX(y, expendituresEU, method="classical") lmCoDaX(y, expendituresEU, method="robust")
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