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robregcc (version 1.1)

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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Install

install.packages('robregcc')

Monthly Downloads

153

Version

1.1

License

GPL (>= 3.0)

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Repository

https://github.com/amishra-stats/robregcc

Homepage

https://arxiv.org/abs/1909.04990

Maintainer

Aditya Mishra

Last Published

July 25th, 2020

Functions in robregcc (1.1)

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

Plot solution path at different value of lambda

Compute solution path of constrained lasso.

Principal sensitivity component analysis with compositional covariates in sparse setting.

Plot residuals estimate from robregcc object

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

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

Plot cross-validation error plot

Robust model estimation approach for regression with compositional covariates.

simulate_robregcc

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

robregcc_option

Control parameter for model estimation:

Simulation data