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$z)
res
#> Online Robust Reduced-Rank Regression
#> ------
#> Stochastic Majorisation-Minimisation
#> ------------
#> Specifications:
#> N P R r initial_size addon
#> 1000 3 1 1 100 10
#>
#> Coefficients:
#> mu A B D Sigma1 Sigma2 Sigma3
#> 1 0.078343 -0.167661 1.553252 0.204748 0.656940 -0.044872 0.050316
#> 2 0.139471 0.442293 0.919832 1.138335 -0.044872 0.657402 -0.063890
#> 3 0.106746 0.801818 -0.693768 1.955019 0.050316 -0.063890 0.698777
plot(res)
newdata <- RRR_sim(A = data$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)