Resist.boot: Run bootstrap on optimized resistance surfaces

In wpeterman/ResistanceGA: Optimize resistance surfaces using genetic algorithms

Run bootstrap on optimized resistance surfaces

Description

This is a 'pseudo' bootstrap procedure that will subsample a specified proportion of the sample locations/individuals, and will refit the MLPE model using the previously optimized resistance surface.

Usage

Resist.boot(mod.names, 
                   dist.mat, 
                   n.parameters, 
                   sample.prop, 
                   iters, 
                   obs, 
                   rank.method, 
                   genetic.mat,
                   keep = NULL,
                   n.cores = NULL)

Arguments

mod.names	A vector of the model names to be assessed
dist.mat	A list containing all distance matrices from optimized resistance surfaces
n.parameters	A vector the length of mod.names, specifying the number of parameters in each model
sample.prop	Proportion of observations to be sampled each iteration (Default = 0.75)
iters	Number of bootstrap iterations to be conducted
obs	Total number of observations (populations or individuals) in your original analysis
rank.method	What metric should be used to rank models during bootstrap analysis? c('AIC', 'AICc', 'R2', 'LL', 'RMSE'). Default = 'AIC'
genetic.mat	Genetic distance matrix without row or column names.
keep	Optional vector of pairwise observations to keep (1) or omit (0)
n.cores	Number of cores to use when running bootstrap in parallel. Default 2 less than total available.

Details

This is a 'pseudo' bootstrap procedure that subsamples distance and genetic matrices, refits the MLPE model for each surface. AICc is calculated based on the number of parameters specified. Ranking of models during the bootstrap analysis is based on the specified rank.method, which defaults to 'AICc'. The objective of this procedure is to identify the surface that is top ranked across all bootstrap iterations.

Value

A data frame reporting the average model weight, average rank, number of times a model was the top model in the set, and the frequency a model was best.

Author(s)

Bill Peterman <[e-mail]>

Examples

 
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wpeterman/ResistanceGA documentation built on Nov. 20, 2023, 11:50 p.m.