Spatial-Temporal Exposure Termination-Maximization Estimates

stap_termination(object, prob = 0.9, exposure_limit = 0.05,
pars = NULL, max_value = NULL, ...)

# S3 method for stapreg
stap_termination(object, prob = 0.9,
exposure_limit = 0.05, pars = NULL, max_value = NULL, ...)

## Arguments

object A fitted model object returned by one of the rstap modeling functions. See stapreg-objects. A number $$p \in (0,1)$$ indicating the desired probability mass to include in the intervals. The default is to report $$90$$% intervals (prob=0.9) rather than the traditionally used $$95$$% (see Details). A number indicating the desired amount of exposure for which the function will return an estimate of distance/time. Note that the exposure_limit corresponds to spatial exposure and 1-temporal exposure. An optional character vector of parameter names. by defuault the max_distance and/or time from the model's original input will be used to calculate the upper bound of possible terminating distances/times - the max_value can be used to specify a new value for this value. Currently ignored.

## Value

A matrix with three columns and as many rows as model parameters (or the subset of parameters specified by pars and/or regex_pars). For a given value of prob, $$p$$, the columns correspond to the lower and upper $$100p$$% interval limits and have the names $$100\alpha/2$$% and $$100(1 - \alpha/2)$$%, where $$\alpha = 1-p$$. For example, if prob=0.9 is specified (a $$90$$% interval), then the column names will be "5%" and "95%", respectively.

## Examples

if (FALSE) {
fit_glm <- stap_glm(formula = y ~ sex + sap(Fast_Food),
subject_data = homog_subject_data,
distance_data = homog_distance_data,
family = gaussian(link = 'identity'),
subject_ID = 'subj_id',
prior = normal(location = 0, scale = 5, autoscale = F),
prior_intercept = normal(location = 25, scale = 5, autoscale = F),
prior_stap = normal(location = 0, scale = 3, autoscale = F),
prior_theta = log_normal(location = 1, scale = 1),
prior_aux = cauchy(location = 0,scale = 5),
max_distance = max(homog_distance_data$Distance), chains = CHAINS, iter = ITER, refresh = -1,verbose = F) terminal_points <- stap_termination(fit_glm, prob = .9, exposure_limit = 0.01) } if (FALSE) { fit_glm <- stap_glm(formula = y ~ sex + sap(Fast_Food), subject_data = homog_subject_data, distance_data = homog_distance_data, family = gaussian(link = 'identity'), subject_ID = 'subj_id', prior = normal(location = 0, scale = 5, autoscale = F), prior_intercept = normal(location = 25, scale = 5, autoscale = F), prior_stap = normal(location = 0, scale = 3, autoscale = F), prior_theta = log_normal(location = 1, scale = 1), prior_aux = cauchy(location = 0,scale = 5), max_distance = max(homog_distance_data$Distance),
chains = CHAINS, iter = ITER,
refresh = -1,verbose = F)
terminal_vals <- stap_termination(fit_glm, prob = .9, exposure_limit = 0.01)
}