This function generates
proto_ipm objects from
Padrino Database tables.
pdb_make_proto_ipm(pdb, ipm_id = NULL, det_stoch = "det", kern_param = "kern")
Optionally, one or more
A vector containing either
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
can also be vectors. These are matched with
ipm_id by position. If the
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.
A list containing one or more
proto_ipms. Names of the list
will correspond to
For more info on
Metcalf et al. (2015). Statistial modeling of annual variation for inference on stochastic population dynamics using Integral Projection Models. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.12405
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