The package overview

This package allows for Gaussian, Logistic and Poisson regression with l1 or l2 penalty. And it can also do fitting and visualizing mixed graphical models.

Examples

## regressions
X <- matrix(rnorm(10 * 50), 10, 50)
y <- rnorm(10)
glmLasso(X, y, lambda = 0.5, family = "gaussian", support_stability = 10)
#> $Coef
#>                    [,1]
#> (Intercept)  0.15629420
#> v1           0.19321508
#> v2           0.00000000
#> v3           0.00000000
#> v4           0.00000000
#> v5           0.00000000
#> v6           0.00000000
#> v7           0.00000000
#> v8           0.00000000
#> v9           0.00000000
#> v10          0.00000000
#> v11          0.00000000
#> v12          0.00000000
#> v13          0.00000000
#> v14          0.00000000
#> v15          0.00000000
#> v16          0.00000000
#> v17          0.00000000
#> v18          0.00000000
#> v19          0.00000000
#> v20          0.00000000
#> v21          0.00000000
#> v22          0.00000000
#> v23          0.00000000
#> v24          0.00000000
#> v25          0.00000000
#> v26          0.00000000
#> v27          0.00000000
#> v28          0.00000000
#> v29          0.00000000
#> v30          0.00000000
#> v31          0.00000000
#> v32         -0.03244359
#> v33          0.00000000
#> v34          0.00000000
#> v35          0.00000000
#> v36          0.00000000
#> v37          0.00000000
#> v38          0.00000000
#> v39          0.00000000
#> v40          0.00000000
#> v41          0.00000000
#> v42          0.00000000
#> v43          0.00000000
#> v44          0.00000000
#> v45          0.00000000
#> v46          0.00000000
#> v47          0.00000000
#> v48          0.00000000
#> v49          0.00000000
#> v50          0.00000000
#> 
glmRidge(X, y, lambda = 0.5, family = "gaussian", thresh = 0.005)
#> $Coef
#>                     [,1]
#> (Intercept)  0.154833686
#> v1           0.143899201
#> v2           0.068139250
#> v3           0.003270413
#> v4           0.019839950
#> v5           0.051049527
#> v6           0.042242864
#> v7           0.029839794
#> v8           0.006610126
#> v9          -0.017951012
#> v10         -0.057385393
#> v11          0.043266154
#> v12          0.025332849
#> v13         -0.033240125
#> v14         -0.001300640
#> v15         -0.027951263
#> v16          0.102459583
#> v17         -0.053749329
#> v18         -0.026374998
#> v19         -0.027111584
#> v20         -0.002631114
#> v21          0.020762640
#> v22          0.045095621
#> v23         -0.027486885
#> v24         -0.001109021
#> v25         -0.015240762
#> v26          0.080959019
#> v27          0.027883094
#> v28         -0.036548396
#> v29          0.025453866
#> v30         -0.011446391
#> v31          0.014010701
#> v32         -0.110409151
#> v33         -0.016069232
#> v34          0.001268198
#> v35         -0.053924697
#> v36          0.070542874
#> v37         -0.008490446
#> v38          0.054554106
#> v39          0.026627567
#> v40          0.023569982
#> v41          0.012806707
#> v42          0.006157437
#> v43          0.002598585
#> v44          0.001624954
#> v45         -0.086349456
#> v46          0.007722239
#> v47          0.016540667
#> v48         -0.032892980
#> v49          0.095891570
#> v50          0.051738818
#> 

## BRAIL
X <- lapply(1:2, function(x) {matrix(rnorm(5 * 10), 5, 10)})
y <- rnorm(5)
BRAIL(X, y, family = "gaussian", tau = 0.8, B = 20, doPar = TRUE)
#> $coefficients
#> $coefficients[[1]]
#>  [1]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
#>  [7]  0.0000000  0.0000000 -0.6189833  0.0000000
#> 
#> $coefficients[[2]]
#>  [1]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
#>  [7]  0.0000000 -0.1057954  0.0000000  0.0000000
#> 
#> 
#> $scores
#> $scores[[1]]
#>  [1] 0.25 0.70 0.05 0.20 0.00 0.00 0.00 0.00 0.85 0.05
#> 
#> $scores[[2]]
#>  [1] 0.00 0.30 0.00 0.65 0.60 0.25 0.70 0.95 0.25 0.55
#> 
#> 

## MixedGraph fitting and plotting
X <- lapply(1 : 2, function(x){matrix(rnorm(12), nrow = 4)})
crf_structure = matrix(c(1, 0, 1, 1), 2, 2)
brail_control <- list(B = 5, tau = 0.6)
G <- MixedGraph(X, crf_structure, brail_control = brail_control)
plot(G, method = "igraph",  weighted = TRUE)