hsstan: Hierarchical shrinkage models
In hsstan: Hierarchical Shrinkage Stan Models for Biomarker Selection

Description Usage Arguments Value See Also Examples

Run the No-U-Turn Sampler (NUTS) as implemented in Stan to fit a hierarchical shrinkage model.

hsstan(
  x,
  covs.model,
  penalized = NULL,
  family = gaussian,
  iter = 2000,
  warmup = floor(iter/2),
  scale.u = 2,
  regularized = TRUE,
  nu = ifelse(regularized, 1, 3),
  par.ratio = 0.05,
  global.df = 1,
  slab.scale = 2,
  slab.df = 4,
  qr = TRUE,
  seed = 123,
  adapt.delta = NULL,
  keep.hs.pars = FALSE,
  ...
)

`x`	Data frame containing outcome, covariates and penalized predictors. Continuous predictors and outcome variable should be standardized before fitting the models as priors assume them to have mean zero and unit variance.
`covs.model`	Formula containing the unpenalized covariates.
`penalized`	Names of the variables to be used as penalized predictors. Any variable that is already part of the `covs.model` formula will be penalized. If `NULL` or an empty vector, a model with only unpenalized covariates is fitted.
`family`	Type of model fitted: either `gaussian()` for linear regression (default) or `binomial()` for logistic regression.
`iter`	Total number of iterations in each chain, including warmup (2000 by default).
`warmup`	Number of warmup iterations per chain (by default, half the total number of iterations).
`scale.u`	Prior scale (standard deviation) for the unpenalized covariates.
`regularized`	If `TRUE` (default), the regularized horseshoe prior is used as opposed to the original horseshoe prior.
`nu`	Number of degrees of freedom of the half-Student-t prior on the local shrinkage parameters (by default, 1 if `regularized=TRUE` and 3 otherwise).
`par.ratio`	Expected ratio of non-zero to zero coefficients (ignored if `regularized=FALSE`). The scale of the global shrinkage parameter corresponds to `par.ratio` divided by the square root of the number of observations; for linear regression only, it's further multiplied by the residual standard deviation `sigma`.
`global.df`	Number of degrees of freedom for the global shrinkage parameter (ignored if `regularized=FALSE`). Larger values induce more shrinkage.
`slab.scale`	Scale of the regularization parameter (ignored if `regularized=FALSE`).
`slab.df`	Number of degrees of freedom of the regularization parameter (ignored if `regularized=FALSE`).
`qr`	Whether the thin QR decomposition should be used to decorrelate the predictors (`TRUE` by default). This is silently set to `FALSE` if there are more predictors than observations.
`seed`	Optional integer defining the seed for the pseudo-random number generator.
`adapt.delta`	Target average proposal acceptance probability for adaptation, a value between 0.8 and 1 (excluded). If unspecified, it's set to 0.99 for hierarchical shrinkage models and to 0.95 for base models.
`keep.hs.pars`	Whether the parameters for the horseshoe prior should be kept in the `stanfit` object returned (`FALSE` by default).
`...`	Further arguments passed to `rstan::sampling()`, such as `chains` (4 by default), `cores` (the value of `options("mc.cores")` by default), `refresh` (`iter / 10` by default).

An object of class hsstan containing the following fields:

`stanfit`	an object of class `stanfit` containing the output produced by Stan, including posterior samples and diagnostic summaries. It can be manipulated using methods from the rstan package.
`betas`	posterior means of the unpenalized and penalized regression parameters.
`call`	the matched call.
`data`	the dataset used in fitting the model.
`model.terms`	a list of names for the outcome variable, the unpenalized covariates and the penalized predictors.
`family`	the `family` object used.
`hsstan.settings`	the optional settings used in the model.

kfold() for cross-validating a fitted object.

data(diabetes)

# non-default settings for speed of the example
df <- diabetes[1:50, ]
hs.biom <- hsstan(df, Y ~ age + sex, penalized=colnames(df)[5:10],
                  chains=2, iter=250)