Fitting Generalized Linear STAP models
stap_glm.fit(y, z, dists_crs, u_s, times_crs, u_t, weight_functions,
stap_data, max_distance = max(dists_crs), max_time = max(times_crs),
weights = rep(1, NROW(y)), offset = rep(0, NROW(y)),
family = stats::gaussian(), ..., prior = normal(),
prior_intercept = normal(), prior_stap = normal(), group = list(),
prior_theta = list(theta_one = normal()), prior_aux = cauchy(location
= 0L, scale = 5L), adapt_delta = NULL)n length vector or n x 2 matrix of outcomes
n x p design matrix of subject specific covariates
(q_s+q_st) x M matrix of distances between outcome observations and built environment features with a hypothesized spatial scale
n x (q *2) matrix of compressed row storage array indices for dists_crs
(q_t+q_st) x M matrix of times where the outcome observations were exposed to the built environment features with a hypothesized temporal scale
n x (q*2) matrix of compressed row storage array indices for times_crs
a Q x 2 matrix with integers coding the appropriate weight function for each STAP
object of class "stap_data" that contains information on all the spatial-temporal predictors in the model
the upper bound on any and all distances included in the model
the upper bound on any and all times included in the model
weights to be added to the likelihood observation for a given subject
offset term to be added to the outcome for a given subject
distributional family - only binomial gaussian or poisson currently allowed
optional arguments passed to the sampler - e.g. iter,warmup, etc.
see stap_glm for more information
list of of group terms from lme4::glmod
See the adapt_delta help page for details.