fits an exponential linear regression model with log link.

nhpp_hmc(formula, data, warm_up, iter_max, seed = NULL)

Arguments

formula

formula specifying the design matrix and outcome akin to glm for more information.

data

data.frame from which to extract outcome and covariates

warm_up

number of iterations in which to tune HMC step-size, these will be discarded

iter_max

total number of samples for which to run sampler

seed

random number generator intializing seed

Details

Fits an exponential linear regression model using a log link via No-U-Turn Sampler (see reference). This model has a \(N(0,\sigma=3)\) prior on all regression coefficients.

References

No U-Turn Sampler Hoffman, M. and Gelman A.(2014). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Journal of Machine Learning Research 15 (1593-1623).