README

The goal of bolasso is to implement model-consistent Lasso estimation via the bootstrap [1].

Installation

# install.packages("devtools")
devtools::install_github("dmolitor/bolasso")

Usage

To illustrate the usage of bolasso, we’ll use the Pima Indians Diabetes dataset to determine which factors are important predictors of testing positive for diabetes. For a full description of the input variables, see the link above.

Load requisite packages and data

library(bolasso)

data(PimaIndiansDiabetes, package = "mlbench")

# Quick overview of the dataset
str(PimaIndiansDiabetes)
#> 'data.frame':    768 obs. of  9 variables:
#>  $ pregnant: num  6 1 8 1 0 5 3 10 2 8 ...
#>  $ glucose : num  148 85 183 89 137 116 78 115 197 125 ...
#>  $ pressure: num  72 66 64 66 40 74 50 0 70 96 ...
#>  $ triceps : num  35 29 0 23 35 0 32 0 45 0 ...
#>  $ insulin : num  0 0 0 94 168 0 88 0 543 0 ...
#>  $ mass    : num  33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 0 ...
#>  $ pedigree: num  0.627 0.351 0.672 0.167 2.288 ...
#>  $ age     : num  50 31 32 21 33 30 26 29 53 54 ...
#>  $ diabetes: Factor w/ 2 levels "neg","pos": 2 1 2 1 2 1 2 1 2 2 ...

First, we run 100-fold bootstrapped Lasso with the glmnet implementation. We can get a rough estimate of the elapsed time using system.time().

system.time({
  model <- bolasso(
    diabetes ~ .,
    data = PimaIndiansDiabetes,
    n.boot = 100, 
    implement = "glmnet",
    family = "binomial"
  )
})
#> Loaded glmnet 4.1-3
#>    user  system elapsed 
#>   19.32    0.14   19.58

model
#> ------------- 100-fold bootstrapped Lasso -------------
#> 
#> Model matrix dimensions:
#>    - 8 Predictors
#>    - 768 Observations
#> 
#> Selected variables:
#>    - 6/8 predictors selected with 90% threshold
#>    - 4/8 predictors selected with 100% threshold

Extracting selected variables

Next, we can extract all variables that were selected in 90% and 100% of the bootstrapped Lasso models. We can also pass any relevant arguments to predict on the cv.glmnet or cv.gamlr model objects. In this case we will use the lambda value that minimizes OOS error.

selected_vars(model, threshold = 0.9, select = "lambda.min")
#> # A tibble: 7 x 2
#>   variable  mean_coef
#>   <chr>         <dbl>
#> 1 Intercept   -8.15  
#> 2 pregnant     0.119 
#> 3 glucose      0.0348
#> 4 pressure    -0.0113
#> 5 mass         0.0821
#> 6 pedigree     0.849 
#> 7 age          0.0138

selected_vars(model, threshold = 1, select = "lambda.min")
#> # A tibble: 5 x 2
#>   variable  mean_coef
#>   <chr>         <dbl>
#> 1 Intercept   -8.15  
#> 2 pregnant     0.119 
#> 3 glucose      0.0348
#> 4 mass         0.0821
#> 5 pedigree     0.849

Plotting selected variables

We can also quickly plot the selected variables at the 90% and 100% threshold values.

plot(model, threshold = 0.9)

plot(model, threshold = 1)

Parallelizing bolasso

We can execute bolasso in parallel via the future package. To do so we can copy the code from above with only one minor tweak shown below.

future::plan("multisession")

system.time({
  model <- bolasso(
    diabetes ~ .,
    data = PimaIndiansDiabetes,
    n.boot = 100, 
    implement = "glmnet",
    family = "binomial"
  )
})
#>    user  system elapsed 
#>    0.17    0.03    5.58

References

[1] Bach, Francis. “Bolasso: Model Consistent Lasso Estimation through the Bootstrap.” ArXiv:0804.1302 [Cs, Math, Stat], April 8, 2008. https://arxiv.org/abs/0804.1302.