complmrob: MM-type estimators for linear regression on compositional...

In complmrob: Robust Linear Regression with Compositional Data as Covariates

MM-type estimators for linear regression on compositional data

Description

Uses the lmrob method for robust linear regression models to fit linear regression models to compositional data.

Usage

complmrob(formula, data)

Arguments

formula	The formula for the regression model
data	The data.frame to use

Details

The variables on the right-hand-side of the formula are transformed with the isometric log-ratio transformation (isomLR) and a robust linear regression model is fit to those transformed variables. The orthonormal basis can be constructed in p different ways, where p is the number of variables on the RHS of the formula.

To get an interpretable estimate of the regression coefficient for each part of the composition, the data is transformed separately for each part. To estimate the coefficient for the *k*-th part, the *k*-th part is used as the orthonormal basis in the transformation and a regression model is fit to this data.

Value

A list of type complmrob with fields

coefficients

the estimated coefficients

models

the single regression models (one for each orthonormal basis)

npred

the number of predictor variables

predictors

the names of the predictor variables

coefind

the index of the relevant coefficient in the single regression models

call

how the function was called

intercept

if an intercept is included

References

K. Hron, P. Filzmoser & K. Thompson (2012): Linear regression with compositional explanatory variables, Journal of Applied Statistics, DOI:10.1080/02664763.2011.644268

Examples

crimes <- data.frame(lifeExp = state.x77[, "Life Exp"],
                     USArrests[ , c("Murder", "Assault", "Rape")])
mUSArr <- complmrob(lifeExp ~ ., data = crimes)
summary(mUSArr)

complmrob documentation built on April 15, 2025, 1:22 a.m.