robCompositions (version 2.1.0)

lmCoDaX: Classical and robust regression of non-compositional (real) response on compositional predictors

Description

Delivers appropriate inference for regression of y on a compositional matrix X.

Usage

lmCoDaX(y, X, method = "robust")

Arguments

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.

Value

An object of class ‘lts’ or ‘lm’ and two summary objects.

Details

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.

References

Filzmoser, P., Hron, K., Thompsonc, K. (2012) Linear regression with compositional explanatory variables. Journal of Applied Statistics, 39, 1115-1128.

See Also

lm

Examples

# NOT RUN {
## 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")
# }