View source: R/stap_termination.R
stap_termination | R Documentation |
Spatial-Temporal Exposure Termination-Maximization Estimates
stap_termination(object, prob = 0.9, exposure_limit = 0.05, pars = NULL, max_value = NULL, ...) ## S3 method for class 'stapreg' stap_termination(object, prob = 0.9, exposure_limit = 0.05, pars = NULL, max_value = NULL, ...)
object |
A fitted model object returned by one of the
rstap modeling functions. See |
prob |
A number p (0 < p < 1) indicating the desired
probability mass to include in the intervals. The default is to report
90% intervals ( |
exposure_limit |
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. |
pars |
An optional character vector of parameter names. |
max_value |
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. |
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 100α/2% and 100(1 - α/2)%, where α
= 1-p. For example, if prob=0.9
is specified (a 90%
interval), then the column names will be "5%"
and "95%"
,
respectively.
## Not run: 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$Distance), chains = CHAINS, iter = ITER, refresh = -1,verbose = F) terminal_points <- stap_termination(fit_glm, prob = .9, exposure_limit = 0.01) ## End(Not run) ## Not run: fit_glm <- stap_glm(formula = y ~ sex + sap(Fast_Food), subject_data = homog_subject_data, distance_data = homog_distance_data, family = gaussian(link = 'identity'), subject_ID = 'subj_id', prior = normal(location = 0, scale = 5, autoscale = F), prior_intercept = normal(location = 25, scale = 5, autoscale = F), prior_stap = normal(location = 0, scale = 3, autoscale = F), prior_theta = log_normal(location = 1, scale = 1), prior_aux = cauchy(location = 0,scale = 5), max_distance = max(homog_distance_data$Distance), chains = CHAINS, iter = ITER, refresh = -1,verbose = F) terminal_vals <- stap_termination(fit_glm, prob = .9, exposure_limit = 0.01) ## End(Not run)
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