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robregcc (version 1.0)

Robust Regression with Compositional Covariates

Description

We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) .

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Version

Install

install.packages('robregcc')

Monthly Downloads

153

Version

1.0

License

GPL (>= 3.0)

Issues

1

Pull Requests

0

Stars

7

Forks

3

Maintainer

Aditya Mishra

Last Published

October 14th, 2019

Functions in robregcc (1.0)

residuals

Extract residuals estimate from the sparse version of the robregcc fitted object.
simulate_robregcc_sp

Simulated date for testing functions in the robregcc package (sparse setting).
simulate_robregcc_nsp

Simulated date for testing functions in the robregcc package (non-sparse setting).
robregcc_sp

Robust model estimation approach for regression with compositional covariates.
robregcc_sim

Simulation data
plot_resid

Plot residuals estimate from robregcc object
cpsc_nsp

Principal sensitivity component analysis with compositional covariates in non-sparse setting.
cpsc_sp

Principal sensitivity component analysis with compositional covariates in sparse setting.
plot_cv

Plot cross-validation error plot
plot_path

Plot solution path at different value of lambda
robregcc_nsp

Robust model estimation approach for regression with compositional covariates.
robregcc_option

Control parameter for model estimation:
coef_cc

Extract coefficients estimate from the sparse version of the robregcc fitted object.
classo

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