complmrob: MM-type estimators for linear regression on compositional...
In complmrob: Robust Linear Regression with Compositional Data as Covariates

Description Usage Arguments Details Value References Examples

View source: R/complmrob.R

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

1	complmrob(formula, data)

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

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.

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

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

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