Fit Survival Model

Fits hierarchical Bayesian survival model using Nimble.

Usage

bb_fit_survival(
  data,
  min_random_year = 5,
  year_trend = FALSE,
  include_uncertain_morts = TRUE,
  year_start = 4L,
  nthin = 10,
  niters = 1000,
  priors = NULL,
  quiet = FALSE
)

Arguments

data

The data.frame.

min_random_year

A whole number of the minimum number of years required to fit year as a random effect (as opposed to a fixed effect).

year_trend

A flag indicating whether to fit a year trend effect. Year trend cannot be fit if there is also a fixed year effect (as opposed to random effect).

include_uncertain_morts

A flag indicating whether to include uncertain mortalities in total mortalities.

year_start

A whole number between 1 and 12 indicating the start of the caribou (i.e., biological) year. By default, April is set as the start of the caribou year.

nthin

A whole number of the thinning rate.

niters

A whole number of the number of iterations per chain after thinning and burn-in.

priors

A named vector of the parameter prior distribution values. Any missing values are assigned their default values in priors_survival() and priors_recruitment(). If NULL, all parameters are assigned their default priors.

quiet

A flag indicating whether to suppress messages and progress bars.

Value

A list of the Nimble model object, data and mcmcr samples.

Details

If the number of years is > min_random_year, a fixed-effects model is fit. Otherwise, a mixed-effects model is fit with random intercept for each year. If year_trend is TRUE and the number of years is > min_random_year, the model will be fit with year as a continuous effect (i.e. trend) and no fixed effect of year. If year_trend is TRUE and the number of years is <= min_random_year, the model will be fit with year as a continuous effect and a random intercept for each year.

The model is always fit with random intercept for each month.

The start month of the Caribou year can be adjusted with year_start.

See also

Other model: bb_fit_recruitment(), bb_fit_recruitment_ml(), bb_fit_survival_ml()

Examples

if (interactive()) {
  fit <- bb_fit_survival(bboudata::bbousurv_a)
}