Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 | ```
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.

1 2 3 4 5 6 7 | ```
## 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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