Introduction to mixedLSR

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Setup

Mixed, low-rank, and sparse multivariate regression (mixedLSR) provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. mixedLSR allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix.

library(mixedLSR)
set.seed(1)

Simulate Data

To demonstrate mixedLSR, we simulate a heterogeneous population where the coefficient matrix is low-rank and sparse and the number of coefficients to estimate is much larger than the sample size.

sim <- simulate_lsr(N = 100, k = 2, p = 30, m = 35)

Compute Model

Then, we compute the model. We limit the number of iterations the model can run.

model <- mixed_lsr(sim$x, sim$y, k = 2, alt_iter = 1, anneal_iter = 10, em_iter = 10, verbose = TRUE)
#> mixedLSR Start: 1 
#> Selecting Lambda..................................................
#> EM Step.....
#> Simulated Annealing Step
#> Full Cycle 1 
#> Computing Final Model...
#> Done!

Clustering Performance

Next, we can evaluate the clustering performance of mixedLSR by viewing a cross-tabulation of the partition labels and by computing the adjusted Rand index (ARI). In this case, mixedLSR perfectly clustered the data.

table(sim$true, model$assign)
#>    
#>      1  2
#>   1 52  0
#>   2  0 48
ari <- mclust::adjustedRandIndex(sim$true, model$assign)
print(paste("ARI:",ari))
#> [1] "ARI: 1"

Coefficient Heatmaps

Lastly, we can view a heatmap of the coefficient matrices and compare them to the true simulated matrices.

plot_lsr(model$a)

plot_lsr(sim$a)

Reproducibility

sessionInfo()
#> R version 4.1.3 (2022-03-10)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19044)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=C                          
#> [2] LC_CTYPE=English_United States.1252   
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C                          
#> [5] LC_TIME=English_United States.1252    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] mixedLSR_0.1.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] highr_0.9         bslib_0.4.0       compiler_4.1.3    pillar_1.8.1     
#>  [5] jquerylib_0.1.4   tools_4.1.3       mclust_5.4.10     digest_0.6.29    
#>  [9] viridisLite_0.4.1 lattice_0.20-45   jsonlite_1.8.1    evaluate_0.16    
#> [13] lifecycle_1.0.2   tibble_3.1.8      gtable_0.3.1      pkgconfig_2.0.3  
#> [17] rlang_1.0.6       Matrix_1.5-1      DBI_1.1.3         cli_3.3.0        
#> [21] rstudioapi_0.14   yaml_2.3.5        xfun_0.33         fastmap_1.1.0    
#> [25] stringr_1.4.1     dplyr_1.0.10      knitr_1.40        generics_0.1.3   
#> [29] sass_0.4.2        vctrs_0.4.2       tidyselect_1.1.2  grid_4.1.3       
#> [33] glue_1.6.2        R6_2.5.1          grpreg_3.4.0      fansi_1.0.3      
#> [37] rmarkdown_2.16    farver_2.1.1      purrr_0.3.4       ggplot2_3.3.6    
#> [41] magrittr_2.0.3    scales_1.2.1      htmltools_0.5.3   MASS_7.3-58.1    
#> [45] assertthat_0.2.1  colorspace_2.0-3  labeling_0.4.2    utf8_1.2.2       
#> [49] stringi_1.7.6     munsell_0.5.0     cachem_1.0.6

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.