bolasso 
The goal of bolasso is to implement model-consistent Lasso estimation via the bootstrap [1].
Installation
You can install the development version from GitHub with:
# 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 19.58We can get a quick overview of the model by printing the bolasso
object.
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% thresholdExtracting 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 0.849Plotting 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")We can now run the code from above, unaltered, and it will execute in parallel.
system.time({
model <- bolasso(
diabetes ~ .,
data = PimaIndiansDiabetes,
n.boot = 100,
implement = "glmnet",
family = "binomial"
)
})
#> user system elapsed
#> 0.17 0.03 5.58References
[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.
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