In FinYang/RRRR: Online Robust Reduced-Rank Regression Estimation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  message = FALSE,
  warning = FALSE
)

RRRR

The R package RRRR provides methods for estimating online Robust Reduced-Rank Regression.

To cite package ‘RRRR’ in publications use:

Yangzhuoran Fin Yang and Ziping Zhao (2023). RRRR: Online Robust Reduced-Rank Regression Estimation. R package version 1.1.1. https://pkg.yangzhuoranyang.com/RRRR/.

Installation

You can install the stable version on R CRAN.

install.packages("RRRR")

You can install the development version from Github with:

# install.packages("devtools")
devtools::install_github("FinYang/RRRR")

Usage

The R package RRRR provides the following estimation methods.

Reduced-Rank Regression using Gaussian MLE: RRR
Robust Reduced-Rank Regression using Cauchy distribution and Majorisation-Minimisation: RRRR
Online Robust Reduced-Rank Regression: ORRRR
- SMM: Stochastic Majorisation-Minimisation
- SAA: Sample Average Approximation
Online update of the above model (except RRR): update.RRRR

See the vignette for a more detailed illustration.

library(RRRR)
set.seed(2222)
data <- RRR_sim()
res <- ORRRR(y=data$y, x=data$x, z=data$z)
res
plot(res)

newdata <- RRR_sim(A = data$spec$A,
                   B = data$spec$B,
                   D = data$spec$D)
res2 <- update(res, newy=newdata$y, newx=newdata$x, newz=newdata$z)
res2
plot(res2)