Last updated: 2022-09-28

Checks: 6 1

Knit directory: lglasso_data_analysis/

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library("lglasso")

Read into the data

Click me 
data=read.csv(file = "./data/ddata.csv")[,-1]

Estimate networks for a given sequence of tuning parameters

Click me 
bic1=c()
rho=seq(0.01,1,length=5)
nu=10
network1=vector("list",length = length(rho))
for (j in 1:length(rho)){
result1=lglasso(data = data, rho = rho[j],heter=T)
network1[[j]]=result1$network
bb1=-2*result1$ll +0.5*nu*length(which(result1$omega!=0))*log(nrow(data))
bic1=c(bic1,bb1)
}

Select the network based on EBIC

Click me 
index=which.min(bic1)
estnetwork=network1[[index]]

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

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] lglasso_0.1.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9       rstudioapi_0.13  knitr_1.39       magrittr_2.0.3  
 [5] workflowr_1.7.0  R6_2.5.1         rlang_1.0.4      fastmap_1.1.0   
 [9] fansi_1.0.3      stringr_1.4.0    tools_4.1.2      xfun_0.31       
[13] utf8_1.2.2       cli_3.3.0        git2r_0.30.1     jquerylib_0.1.4 
[17] htmltools_0.5.2  ellipsis_0.3.2   rprojroot_2.0.3  yaml_2.3.5      
[21] digest_0.6.29    tibble_3.1.7     lifecycle_1.0.1  later_1.3.0     
[25] sass_0.4.2       vctrs_0.4.1      promises_1.2.0.1 fs_1.5.2        
[29] cachem_1.0.6     glue_1.6.2       evaluate_0.15    rmarkdown_2.14  
[33] stringi_1.7.6    bslib_0.4.0      compiler_4.1.2   pillar_1.8.0    
[37] jsonlite_1.8.0   httpuv_1.6.5     glasso_1.11      pkgconfig_2.0.3