This code contains an example of running the glmm function.

# Libraries
library(glmmPen)
## Warning: package 'glmmPen' was built under R version 4.1.3
## Loading required package: lme4
## Warning: package 'lme4' was built under R version 4.1.2
## Loading required package: Matrix
## Warning: package 'Matrix' was built under R version 4.1.2
## Loading required package: bigmemory
## Warning: package 'bigmemory' was built under R version 4.1.2
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.1.2
library(stringr)
## Warning: package 'stringr' was built under R version 4.1.2
# Example of running glmm() function on basal dataset - fit a single model with
#   no variable selection

# basal data from glmmPen package
data("basal")

# Extract response
y = basal$y
# Select a sampling of 10 TSP covariates from the total 50 covariates
set.seed(1618)
idx = sample(1:50, size = 10, replace = FALSE)
# Selected column index values:
(idx = idx[order(idx)])
##  [1]  3  6 16 18 27 28 29 30 34 41
X = basal$X[,idx]
# Selected predictors:
colnames(X)
##  [1] "GPR160_CD109"  "SPDEF_MFI2"    "CHST6_CAPN9"   "SLC40A1_CDH3" 
##  [5] "PLEK2_HSD17B2" "GPX2_ERO1L"    "CYP3A5_B3GNT5" "LY6D_ATP2C2"  
##  [9] "MYO1A_FGFBP1"  "CTSE_COL17A1"
group = basal$group
# Levels of the grouping variable:
levels(group)
## [1] "UNC_PDAC"     "TCGA_PDAC"    "TCGA_Bladder" "UNC_Breast"
start_glmm = proc.time()
set.seed(1618)
fit_glmm = glmm(formula = y ~ X + (X | group), 
                family = "binomial", covar = "independent", 
                optim_options = optimControl())
## recommended starting variance: 0.500000
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10         10 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10         10 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.000000 302.000000   0.024135  10.000000  10.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.000000 332.000000   0.015629  10.000000  10.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.000000 365.000000   0.011229  10.000000  10.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  8.108e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  6.299e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  5.482e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  4.416e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  3.419e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  3.096e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.20e+01   7.12e+02   2.34e-03   1.00e+01   1.00e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  1.772e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.977e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  2.098e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  1.674e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  1.250e+03  1.234e-03  1.000e+01  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  1.001e-03  1.000e+01  1.000e+01
## Start of sampling from posterior
## Finished sampling from posterior
## Pajor Log-Likelihood Calculation
end_glmm = proc.time()
end_glmm - start_glmm
##    user  system elapsed 
##  151.48   24.35  180.55
sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [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] stringr_1.4.0    glmmPen_1.5.3.4  Rcpp_1.0.7       bigmemory_4.5.36
## [5] lme4_1.1-27.1    Matrix_1.3-4    
## 
## loaded via a namespace (and not attached):
##  [1] bdsmatrix_1.3-6      mvtnorm_1.1-3        lattice_0.20-44     
##  [4] prettyunits_1.1.1    ps_1.6.0             V8_3.6.0            
##  [7] digest_0.6.29        utf8_1.2.2           R6_2.5.1            
## [10] plyr_1.8.6           stats4_4.1.1         bigmemory.sri_0.1.3 
## [13] evaluate_0.14        ggplot2_3.4.2        pillar_1.6.4        
## [16] rlang_1.1.0          curl_4.3.2           rstudioapi_0.13     
## [19] minqa_1.2.4          callr_3.7.0          nloptr_1.2.2.3      
## [22] jquerylib_0.1.4      rmarkdown_2.11       splines_4.1.1       
## [25] loo_2.4.1            munsell_0.5.0        compiler_4.1.1      
## [28] xfun_0.28            rstan_2.21.2         pkgconfig_2.0.3     
## [31] pkgbuild_1.3.0       rstantools_2.1.1     htmltools_0.5.4     
## [34] tidyselect_1.1.1     gridExtra_2.3        tibble_3.1.6        
## [37] codetools_0.2-18     matrixStats_0.61.0   fansi_0.5.0         
## [40] withr_2.5.0          crayon_1.4.2         dplyr_1.0.9         
## [43] MASS_7.3-54          grid_4.1.1           nlme_3.1-152        
## [46] jsonlite_1.7.2       gtable_0.3.0         lifecycle_1.0.3     
## [49] magrittr_2.0.1       StanHeaders_2.21.0-7 scales_1.2.1        
## [52] RcppParallel_5.1.4   cli_3.3.0            stringi_1.7.6       
## [55] cachem_1.0.6         reshape2_1.4.4       coxme_2.2-18.1      
## [58] bslib_0.4.2          ellipsis_0.3.2       generics_0.1.2      
## [61] vctrs_0.6.2          boot_1.3-28          tools_4.1.1         
## [64] ncvreg_3.13.0        glue_1.6.2           purrr_0.3.4         
## [67] parallel_4.1.1       processx_3.5.2       fastmap_1.1.0       
## [70] survival_3.5-5       yaml_2.2.1           inline_0.3.19       
## [73] colorspace_2.0-2     knitr_1.36           sass_0.4.4