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This repository has been archived by the owner on Jun 13, 2023. It is now read-only.
Jim Thorson edited this page May 10, 2019 · 6 revisions

Frequently asked questions

During install I get a weird error message with the words LOCK or UNLOCK in the terminal message

Please try installing e.g. using:

devtools::install_github("james-thorson/VAST", INSTALL_opts = c('--no-lock'))

How should I decide how many "knots" (n_x) to use?

I recommend using relatively fewer knots during model development (e.g., n_x=250), and then increasing for a final usage (publication or assessment documents, n_x=1000). Before final usage, I also recommend slightly increasing the number of knots (n_x=1100) to confirm that results have stabilized and are not sensitive to small changes in the decision about the number of knots.

What are the definitions for encounter probability and positive catch rates?

SpatialDeltaGLMM implements a delta-model. Delta-models are used to analyze catch data in units biomass, where the response (biomass) is always either continuous and positive valued (weight of a species in a given sample), or zero (the species is not encountered in that sample). Encounter probability is the predicted probability of encountering the species, while positive catch rate is the expected catch in weight for that sample if the species is encountered. Encounter probability are typically each modeled independently using a generalized linear mixed model. The product of encounter probability and positive catch rate is therefore the expected catch in weight for a given sample, and is equal to local density times local catchability.

I'm getting an error during Data_Fn about Some years and/or categories have either all or no encounters: what should I do?

As the message states, this error indicates that you have either 0% or 100% encounters in one or more year for at least one species. This will interfere with default model settings, and I have therefore added an error and informative message. There are several potential solutions, and each is explained here