Resist.boot: Run bootstrap on optimized resistance surfaces

Resist.bootR Documentation

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 <Peterman.73@osu.edu>

Examples

 
## Not run:
## *** TO BE COMPLETED *** ##

## End (Not run)

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