Set up a lasso prior in brms
Function used to set up a lasso prior for population-level effects in brms. The function does not evaluate its arguments – it exists purely to help set up the model.
lasso(df = 1, scale = 1)
df |
Degrees of freedom of the chi-square prior of the inverse tuning
parameter. Defaults to |
scale |
Scale of the lasso prior. Defaults to |
The lasso prior is the Bayesian equivalent to the LASSO method for performing
variable selection (Park & Casella, 2008).
With this prior, independent Laplace (i.e. double exponential) priors
are placed on the population-level effects.
The scale of the Laplace priors depends on a tuning parameter
that controls the amount of shrinkage. In brms, the inverse
of the tuning parameter is used so that smaller values imply
more shrinkage. The inverse tuning parameter has a chi-square distribution
and with degrees of freedom controlled via argument df
of function lasso (defaults to 1). For instance,
one can specify a lasso prior using set_prior("lasso(1)").
To make sure that shrinkage can equally affect all coefficients,
predictors should be one the same scale.
If you do not want to standardized all variables,
you can adjust the general scale of the lasso prior via argument
scale, for instance, lasso(1, scale = 10).
A character string obtained by match.call() with
additional arguments.
Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681-686.
set_prior(lasso(df = 1, scale = 10))
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