robregcc (version 1.0)

classo: Estimate parameters of linear regression model with compositional covariates using method suggested by Pixu shi.

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

# NOT RUN {
library(robregcc)
library(magrittr)

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

# Predictor transformation due to compositional constraint:
# Equivalent to performing centered log-ratio transform 
Xt <- svd(t(C))$u %>% tcrossprod() %>% subtract(diag(p),.) %>% crossprod(t(X),.)
#
Xm <- colMeans(Xt)
Xt <- scale(Xt,Xm,FALSE)                  # centering of predictors 
mean.y <- mean(y)
y <- y - mean.y                           # centering of response 
Xt <- cbind(1,Xt)                         # 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) 
# }