fit_binary: Fit binary outcome model in TMB

In taylorokonek/stbench: Fully Bayesian Benchmarking for spatio-temporal models

Fit binary outcome model in TMB

Description

Fit binary outcome model in TMB. Currently can only fit a space-only model at a single time point. Linear predictor includes a single intercept and a BYM2 sptial random effect (and a IID cluster-level random effect if a lono-binomial likelihood is chosen). Can fit benchmarked models, unbenchmarked models, or both. Simultaneous benchmarking is performed via a second likelihood for the national level estimate. Produces posterior samples for fitted values, hyperparameters, random effects, and fixed effects. All models use either a lono-binomial likelihood, described in Dong and Wakefield (2021) or a betabinomial likelihood at the cluster level.

Usage

fit_binary(
  binom_df,
  cluster = "cluster",
  y = "y",
  Ntrials = "N",
  region = "admin1",
  hiv_adj = NA,
  natl = NULL,
  natl_sd = NULL,
  pop_weights = NULL,
  Q_struct_space = NULL,
  intercept_pri = c(0, 31.62278),
  nsamp = 1000,
  benched = "unbenched",
  family = "binomial"
)

Arguments

binom_df	a dataframe containing binomial counts, and the following columns: cluster: cluster ID y: deaths Ntrials: total number of person-months in this age, cluster, and region region: area
cluster	the column in binom_df corresponding to cluster id
y	the column in binom_df corresponding to deaths
Ntrials	the column in binom_df corresponding to total number of person-months
region	the column in binom_df corresponding to region/area. Must be numeric and start from 1.
hiv_adj	An optional log offset in time to include in the linear predictor. Defaults to no log offset.
natl	a vector of national level estimates, arranged in order of time
natl_sd	a vector of standard deviations for national level estimates, arranged in order of time
pop_weights	a vector of population weights for use in the benchmarking constraint. Must sum to one at each time point, and be in order arrange(region)
Q_struct_space	An ICAR precision matrix. Should be unscaled, as scaling will happen internally.
intercept_pri	Prior specification for the intercept. Defaults to c(0, 31.62278), corresponding to the default prior for the intercept in INLA, with mean 0 and precision 0.001. Must be a vector of length 2, with specificaiton c(mean, sd) for a Normal distribution. Currently only an option for unbenchmarked models.
nsamp	Number of posterior samples to take from joint posterior. Defaults to 1000
benched	A string, either "benched", "unbenched", or "both", determining whether to fit a benchmarked model, and unbenchmarked model, or both. Defaults to "unbenched".
family	A string, either "binomial" or "betabinomial", specifying which likelihood to use for the clustered observations. If "binomial" is specified, does the lono-binomial correction, as described in Dong and Wakefield (2021). Defaults to "binomial".

Value

A list containing:

• fitted_mat: a matrix of posterior samples of fitted values in order arrange(region, time)

• re_list: a list contaning matrices of posterior samples for each random effect term

• param_list: a list containing matrices of posterior samples for fixed effects and hyperparameters

• runtime: the time it took to fit the model in TMB and get samples from the joint posterior

If benched = "both", a list of two will be returned containing the above list for both benchmarked and unbenchmarked models.

Author(s)

Taylor Okonek

taylorokonek/stbench documentation built on Jan. 7, 2025, 11:13 p.m.