bam_star: Fit Bayesian Additive STAR Model with MCMC

In countSTAR: Flexible Modeling of Count Data

Fit Bayesian Additive STAR Model with MCMC

Description

Run the MCMC algorithm for a STAR Bayesian additive model The transformation can be known (e.g., log or sqrt) or unknown (Box-Cox or estimated nonparametrically) for greater flexibility.

Usage

bam_star(
  y,
  X_lin,
  X_nonlin,
  splinetype = "orthogonal",
  transformation = "np",
  y_max = Inf,
  nsave = 5000,
  nburn = 5000,
  nskip = 2,
  save_y_hat = FALSE,
  verbose = TRUE
)

Arguments

y	n x 1 vector of observed counts
X_lin	n x pL matrix of predictors to be modelled as linear
X_nonlin	n x pNL matrix of predictors to be modelled as nonlinear
splinetype	Type of spline to use for modelling the nonlinear predictors; must be either "orthogonal" (orthogonalized splines–the default) or "thinplate" (low-rank thin plate splines)
transformation	transformation to use for the latent data; must be one of "identity" (identity transformation) "log" (log transformation) "sqrt" (square root transformation) "np" (nonparametric transformation estimated from empirical CDF) "pois" (transformation for moment-matched marginal Poisson CDF) "neg-bin" (transformation for moment-matched marginal Negative Binomial CDF) "box-cox" (box-cox transformation with learned parameter) "ispline" (transformation is modeled as unknown, monotone function using I-splines)
y_max	a fixed and known upper bound for all observations; default is Inf
nsave	number of MCMC iterations to save
nburn	number of MCMC iterations to discard
nskip	number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw
save_y_hat	logical; if TRUE, compute and save the posterior draws of the expected counts, E(y), which may be slow to compute
verbose	logical; if TRUE, print time remaining

Details

STAR defines a count-valued probability model by (1) specifying a Gaussian model for continuous *latent* data and (2) connecting the latent data to the observed data via a *transformation and rounding* operation.

Posterior and predictive inference is obtained via a Gibbs sampler that combines (i) a latent data augmentation step (like in probit regression) and (ii) an existing sampler for a continuous data model.

There are several options for the transformation. First, the transformation can belong to the *Box-Cox* family, which includes the known transformations 'identity', 'log', and 'sqrt', as well as a version in which the Box-Cox parameter is inferred within the MCMC sampler ('box-cox'). Second, the transformation can be estimated (before model fitting) using the empirical distribution of the data y. Options in this case include the empirical cumulative distribution function (CDF), which is fully nonparametric ('np'), or the parametric alternatives based on Poisson ('pois') or Negative-Binomial ('neg-bin') distributions. For the parametric distributions, the parameters of the distribution are estimated using moments (means and variances) of y. Third, the transformation can be modeled as an unknown, monotone function using I-splines ('ispline'). The Robust Adaptive Metropolis (RAM) sampler is used for drawing the parameter of the transformation function.

Value

a list with at least the following elements:

• coefficients: the posterior mean of the coefficients

• fitted.values: the posterior mean of the conditional expectation of the counts y

• post.coefficients: posterior draws of the coefficients

• post.fitted.values: posterior draws of the conditional mean of the counts y

• post.pred: draws from the posterior predictive distribution of y

• post.lambda: draws from the posterior distribution of lambda

• post.sigma: draws from the posterior distribution of sigma

• post.log.like.point: draws of the log-likelihood for each of the n observations

• WAIC: Widely-Applicable/Watanabe-Akaike Information Criterion

• p_waic: Effective number of parameters based on WAIC

In the case of transformation="ispline", the list also contains

• post.g: draws from the posterior distribution of the transformation g

• post.sigma.gamma: draws from the posterior distribution of sigma.gamma, the prior standard deviation of the transformation g() coefficients

Examples

# Simulate data with count-valued response y:
sim_dat = simulate_nb_friedman(n = 100, p = 5, seed=32)
y = sim_dat$y; X = sim_dat$X

# Linear and nonlinear components:
X_lin = as.matrix(X[,-(1:3)])
X_nonlin = as.matrix(X[,(1:3)])

# STAR: nonparametric transformation
fit <- bam_star(y,X_lin, X_nonlin, nburn=1000, nskip=0)

# Posterior mean of each coefficient:
coef(fit)

# WAIC:
fit$WAIC

# MCMC diagnostics:
plot(as.ts(fit$post.coefficients[,1:3]))

# Posterior predictive check:
hist(apply(fit$post.pred, 1,
           function(x) mean(x==0)), main = 'Proportion of Zeros', xlab='');
abline(v = mean(y==0), lwd=4, col ='blue')

countSTAR documentation built on July 9, 2023, 5:12 p.m.