R: Classical and robust regression of non-compositional response...

lmCoDaX {robCompositions} | R Documentation

## Classical and robust regression of non-compositional 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.

### 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 isometric logratio (ilr) transformation is used.

### Value

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

### Author(s)

Peter Filzmoser

### 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

    ## 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")

[Package robCompositions version 2.0.9 Index]