Skip to content

Latest commit

 

History

History
105 lines (82 loc) · 3.4 KB

README.md

File metadata and controls

105 lines (82 loc) · 3.4 KB
Raw

RRRR

CRAN_Status_Badge Monthly_Downloads Licence R-CMD-check

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.

  1. Reduced-Rank Regression using Gaussian MLE: RRR
  2. Robust Reduced-Rank Regression using Cauchy distribution and Majorisation-Minimisation: RRRR
  3. Online Robust Reduced-Rank Regression: ORRRR
    • SMM: Stochastic Majorisation-Minimisation
    • SAA: Sample Average Approximation
  4. 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
#> Online Robust Reduced-Rank Regression
#> ------
#> Stochastic Majorisation-Minimisation
#> ------------
#> Specifications:
#>            N            P            R            r initial_size        addon 
#>         1000            3            1            1          100           10 
#> 
#> Coefficients:
#>          mu         A         B         D    Sigma1    Sigma2    Sigma3
#> 1  0.078343 -0.167661  1.553252  0.204748  0.656940 -0.044872  0.050316
#> 2  0.139471  0.442293  0.919832  1.138335 -0.044872  0.657402 -0.063890
#> 3  0.106746  0.801818 -0.693768  1.955019  0.050316 -0.063890  0.698777
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
#> Online Robust Reduced-Rank Regression
#> ------
#> Stochastic Majorisation-Minimisation
#> ------------
#> Specifications:
#>            N            P            R            r initial_size        addon 
#>         2000            3            1            1         1010           10 
#> 
#> Coefficients:
#>          mu         A         B         D    Sigma1    Sigma2    Sigma3
#> 1  0.073939 -0.159814  1.520309  0.208943  0.675436 -0.021789  0.040888
#> 2  0.142791  0.450992  0.962698  1.117024 -0.021789  0.679136 -0.024140
#> 3  0.107647  0.817590 -0.670435  1.957084  0.040888 -0.024140  0.703949
plot(res2)

License

This package is free and open source software, licensed under GPL-3.