Predictions based on SDM.

Considering a new dataset (across the same sites), computes predictions based on the SDM passed as sdm.

Usage

# S3 method for class 'sdm'
predict(object, new_data, type = "predictive", seed = 1, cores = 1, ...)

predict_sdm(...)

Arguments

object

An sdm object containing the output of a fit_sdm call.

new_data

a data.frame with the dataset at which we wish to obtain predictions.

type

type of predictions to be computed. Admitted values are

• "predictive" (default): posterior predictive distribution;

• "expected": theoretical mean of the posterior predictive distribution;

• "latent": latent density (i.e., disregarding the observation error);

seed

a seed used for the predictions. predictions are obtained through Monte Carlo samples from the posterior predictive distribution. Therefore, a seed is needed to ensure the results' reproducibility.

cores

number of threads used to compute the predictions. If four chains were used in the sdm, then four (or less) threads are recommended.

...

params to be passed to predict.sdm

Value

An object of class CmdStanGQ (from the instantiate package) containing samples for the posterior predictive distribution for predictions.

Details

The current version of the code assumes the data where predictions are needed is ordered by "site" and "site" and, in addition, its sites MUST be the same as the ones used to obtain the parameters' estimates from the the sdm object.

See also

fit_sdm()

Author

lcgodoy