classo: Estimate parameters of linear regression model with...

In robregcc: Robust Regression with Compositional Covariates

Description

The model uses scaled lasoo approach for model selection.

Usage

classo(Xt, y, C, we = NULL, type = 1, control = list())

Arguments

Xt	CLR transformed predictor matrix.
y	model response vector
C	sub-compositional matrix
we	specify weight of model parameter
type	1/2 for l1 / l2 loss in the model
control	a list of internal parameters controlling the model fitting

Value

beta	model parameter estimate

References

Shi, P., Zhang, A. and Li, H., 2016. Regression analysis for microbiome compositional data. The Annals of Applied Statistics, 10(2), pp.1019-1040.

Examples

library(robregcc)
library(magrittr)

data(simulate_robregcc)
X <- simulate_robregcc$X;
y <- simulate_robregcc$y
C <- simulate_robregcc$C
n <- nrow(X); p <- ncol(X); k <-  nrow(C)

# Predictor transformation due to compositional constraint:
Xt <- cbind(1,X)          # accounting for intercept in predictor
C <- cbind(0,C)            # accounting for intercept in constraint
bw <- c(0,rep(1,p))        # weight matrix to not penalize intercept 

# Non-robust regression, [Pixu Shi 2016]
control <- robregcc_option(maxiter = 5000, tol = 1e-7, lminfac = 1e-12)
fit.nr <- classo(Xt, y, C, we = bw, type = 1, control = control) 

robregcc documentation built on July 26, 2020, 1:07 a.m.