Fitting Generalized Linear STAP models

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)

Arguments

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