glm_nab: Fit GLM with the 'naive adaptive bayes' prior

In umich-biostatistics/AdaptiveBayesianUpdates: Adaptive Bayesian Updates

Fit GLM with the 'naive adaptive bayes' prior

Description

Program for fitting a GLM equipped with the 'naive adaptive bayes' prior evaluated in the manuscript.

Usage

glm_nab(
  y,
  x_standardized,
  family = "binomial",
  alpha_prior_mean,
  alpha_prior_cov,
  phi_dist = "trunc_norm",
  phi_mean = 1,
  phi_sd = 0.25,
  eta_param = 2.5,
  beta_orig_scale,
  beta_aug_scale,
  beta_aug_scale_tilde,
  local_dof = 1,
  global_dof = 1,
  slab_dof = Inf,
  slab_scale = 15,
  mu_sd = 5,
  only_prior = F,
  mc_warmup = 1000,
  mc_iter_after_warmup = 1000,
  mc_chains = 1,
  mc_thin = 1,
  mc_stepsize = 0.1,
  mc_adapt_delta = 0.9,
  mc_max_treedepth = 15,
  return_as_CmdStanMCMC = FALSE,
  eigendecomp_hist_var = NULL,
  scale_to_variance225 = NULL,
  seed = sample.int(.Machine$integer.max, 1),
  slab_precision = NULL
)

Arguments

y	(vector) outcomes corresponding to the type of glm desired. This should match whatever datatype is expected by the stan program.
x_standardized	(matrix) matrix of numeric values with number of rows equal to the length of y and number of columns equal to p+q. It is assumed without verification that each column is standardized to whatever scale the prior expects - in Boonstra and Barbaro, all predictors are marginally generated to have mean zero and unit variance, so no standardization is conducted. In practice, all data should be standardized to have a common scale before model fitting. If regression coefficients on the natural scale are desired, they can be easily obtained through unstandardizing.
family	(character) Similar to argument in glm with the same name, but here this must be a character, and currently only 'binomial' (if y is binary) or 'gaussian' (if y is continuous) are valid choices.
alpha_prior_mean	(vector) p-length vector giving the mean of alpha from the historical analysis, corresponds to m_alpha in Boonstra and Barbaro
alpha_prior_cov	(matrix) pxp positive definite matrix giving the variance of alpha from the historical analysis, corresponds to S_alpha in Boonstra and Barbaro
phi_dist	(character) the name of the distribution to use as a prior on phi. This must be either 'trunc_norm' or 'beta'.
phi_mean	see phi_sd
phi_sd	(real) prior mean and standard deviation of phi. At a minimum, phi_mean must be between 0 and 1 (inclusive) and phi_sd must be non-negative (you can choose phi_sd = 0, meaning that phi is identically equal to phi_mean). If 'phi_dist' is 'trunc_norm', then 'phi_mean' and 'phi_sd' are interpreted as the parameters of the untruncated normal distribution and so are not actually the parameters of the resulting distribution after truncating phi to the 0,1 interval. If 'phi_dist' is 'beta', then 'phi_mean' and 'phi_sd' are interpreted as the literal mean and standard deviation, from which the shape parameters are calculated. When 'phi_dist' is 'beta', not all choices of 'phi_mean' and 'phi_sd' are valid, e.g. the standard deviation of the beta distribution must be no greater than sqrt(phi_mean * (1 - phi_mean)). Also, the beta distribution is difficult to sample from if one or both of the shape parameters is much less than 1. An error will be thrown if an invalid parameterization is provided, and a warning will be thrown if a parameterization is provided that is likely to result in a "challenging" prior.
eta_param	(real) prior hyperparmeter for eta, which scales the alpha_prior_cov in the adaptive prior contribution and is apriori distributed as an inverse-gamma random variable. Specifically, eta_param is a common value for the shape and rate of the inverse-gamma, meaning that larger values cause the prior distribution of eta to concentrate around one. You may choose eta_param = Inf to make eta identically equal to 1
beta_orig_scale	see beta_aug_scale
beta_aug_scale	(pos. real) constants indicating the prior scale of the horseshoe. Both values correspond to 'c / sigma' in the notation of Boonstra and Barbaro, because that paper never considers beta_orig_scale!=beta_aug_scale. Use the function solve_for_hiershrink_scale to calculate this quantity. If 'y' is binary, then sigma doesn't actually exist as a parameter, and it will be set equal to 2 inside the function. If 'y' is continuous, then sigma is equipped with its own weak prior. In either case, it is not intended that the user scale by sigma "manually".
beta_aug_scale_tilde	(pos. real) constant indicating the prior scale of the horseshoe for the augmented covariates when phi = 1, i.e. when the historical analysis is fully used. This corresponds to tilde_c in Boonstra and Barbaro
local_dof	(pos. integer) number indicating the degrees of freedom for lambda_j. Boonstra and Barbaro always used local_dof = 1. Choose a negative value to tell the function that there are no local hyperparameters.
global_dof	(pos. integer) number indicating the degrees of freedom for tau. Boonstra and Barbaro always used global_dof = 1. Choose a negative value to tell the function that there is no global hyperparameter.
slab_dof	see slab_scale
slab_scale	(pos. real) these control the slab-part of the regularized horseshoe. Specifically, in the notation of Boonstra and Barbaro, d^2~InverseGamma(slab_dof/2, slab_scale^2*slab_dof/2). In Boonstra and Barbaro, d was fixed at 15, and you can achieve this by leaving these at their default values of slab_dof = Inf and slab_scale = 15.
mu_sd	(pos. real) the prior standard deviation for the intercept parameter mu
only_prior	(logical) should all data be ignored, sampling only from the prior?
mc_warmup	number of MCMC warm-up iterations
mc_iter_after_warmup	number of MCMC iterations after warm-up
mc_chains	number of MCMC chains
mc_thin	every nth draw to keep
mc_stepsize	positive stepsize
mc_adapt_delta	between 0 and 1
mc_max_treedepth	max tree depth
return_as_CmdStanMCMC	(logical) should the function return the CmdStanMCMC object asis or should a summary of CmdStanMCMC be returned as a regular list
eigendecomp_hist_var	R object of class 'eigen' containing a pxp matrix of eigenvectors in each row (equivalent to v_0 in Boonstra and Barbaro) and a p-length vector of eigenvalues. This is by default equal to eigen(alpha_prior_cov)
scale_to_variance225	a vector assumed to be such that, when multiplied by the diagonal elements of alpha_prior_cov, the result is a vector of elements each equal to 225. This is explicitly calculated if it is not provided
seed	seed for the underlying STAN model to allow for reproducibility
slab_precision	(pos. real) the slab-part of the regularized horseshoe, this is equivalent to (1/d)^2 in the notation of Boonstra and Barbaro. If specified, it is assumed that you want a fixed slab component and will take precedence over any provided values of slab_dof and slab_scale; slab_precision is provided for backwards compatibility but will be going away in a future release, and the proper way to specify a fixed slab component with with precision 1/d^2 for some number d is through slab_dof = Inf and slab_scale = d.

Value

list object containing the draws and other information.

Examples

data(current)

alpha_prior_cov = matrix(data = c(0.02936, -0.02078, 0.00216, -0.00637,
                                  -0.02078, 0.03192, -0.01029, 0.00500,
                                  0.00216, -0.01029, 0.01991, -0.00428,
                                  -0.00637, 0.00500, -0.00428, 0.01650),
                         byrow = FALSE, nrow = 4);
scale_to_variance225 = diag(alpha_prior_cov) / 225;
eigendecomp_hist_var = eigen(alpha_prior_cov);

foo = glm_nab(y = current$y_curr,
              x_standardized = current[,2:11],
              family = "binomial",
              alpha_prior_mean = c(1.462, -1.660, 0.769, -0.756),
              alpha_prior_cov = alpha_prior_cov,
              phi_dist = "trunc_norm",
              phi_mean = 1,
              phi_sd = 0.25,
              eta_param = 2.5,
              beta_orig_scale = 0.0223,
              beta_aug_scale = 0.0223,
              beta_aug_scale_tilde = 0.05,
              local_dof = 1,
              global_dof = 1,
              mu_sd = 5,
              only_prior = 0,
              mc_warmup = 200,
              mc_iter_after_warmup = 200,
              mc_chains = 2,
              mc_thin = 1,
              mc_stepsize = 0.1,
              mc_adapt_delta = 0.999,
              mc_max_treedepth = 15,
              eigendecomp_hist_var = eigendecomp_hist_var,
              scale_to_variance225 = scale_to_variance225);

umich-biostatistics/AdaptiveBayesianUpdates documentation built on April 5, 2024, 2:11 a.m.