R: Generate proto_ipms from Padrino objects

pdb_make_proto_ipm {Rpadrino}

R Documentation

Generate proto_ipms from Padrino objects

Description

This function generates proto_ipm objects from Padrino Database tables.

Usage

pdb_make_proto_ipm(pdb, ipm_id = NULL, det_stoch = "det", kern_param = "kern")

Arguments

`pdb`	A `pdb` object.
`ipm_id`	Optionally, one or more `ipm_id`'s to build. If empty, all models contained in the `pdb` object will be processed into `proto_ipm`'s.
`det_stoch`	A vector containing either `"det"` or `"stoch"`. This determines whether we want to construct a deterministic or stochastic model. Default is `"det"`. See details
`kern_param`	If `det_stoch = "stoch"`, then whether or not to construct a kernel resampled model, or a parameter resampled model. See details.

Details

proto_ipm objects contain all of the information needed to implement an IPM, but stop short of actually generating kernels. These are intermediate building blocks that can be modified before creating a full IPM so that things like perturbation analysis are a bit more straightforward.

When requesting many models, the det_stoch and kern_param parameters can also be vectors. These are matched with ipm_id by position. If the lengths of det_stoch and kern_param do not match the length ipm_id, they will be recycled until they do.

For stochastic models, there is sometimes the option of building either a kernel-resampled or a parameter resampled model. A kernel resampled model uses some point estimate for time and/or space varying parameters to generate kernels for each year/site/grouping factor. Parameter resampled models sample parameters from distributions. Padrino stores this information for some models when it is available in the literature, and tries to fail informatively when these distributions aren't available in the database.

Value