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Fit the Lewontin Cohen model.

run_lc_model_univariate.Rd

Fit the Bernoulli Stan model and return posterior summaries.

Usage

run_lc_model_univariate(occ, ts, grainsize = 10, ...)

Arguments

occ

Numeric vector of presence absence observations (zeroes and ones).

ts

Numeric array of environmental variables. It has dimensions M (sites), N (time steps) and P (environmental variables)

grainsize

The grainsize for the reduce sum parallelization inerly handle by stan code. Ideally is the

...

Named arguments to the `sample()` method of CmdStan model quotient between the number of observations and the number of cores. objects: <https://mc-stan.org/cmdstanr/reference/model-method-sample.html>

Value

A data frame of posterior summaries.

See also

Other models: lc_multivariate(), lc_univariate()

Examples

if (instantiate::stan_cmdstan_exists()) {
 occ <- mus_virtualis
 bio1_ts <- terra::unwrap(xsdm::cmcc_cm_bio1)
 bio12_ts <- terra::unwrap(xsdm::cmcc_cm_bio12)
 envData <- list(bio1 = bio1_ts)
 ts <- envDataArray(occ, envData)
 run_lc_model_univariate(
  occ = occ$presence,
  ts = ts
  )
}