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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Define "M" under different ways is matter to calibration models in ecological niche model, we used buffer zone as calibration zone, based on:
These function help you to reduce environmental.
library(sdStaf) data(phytotoma)
Now, we need to load environmental dataset.
library(dismo) predictor <- stack(list.files(path=paste(system.file(package="dismo"),'/ex', sep=''), pattern='grd', full.names=TRUE )) # Read names names(predictor) plot(predictor$bio1)
Next function, reduce environmental data based on buffer zone and customer zone.
buf.M <- stim.M(phytotoma[,2:3], radio = 131) reduce_cut <- reduce.env(env = predictor, occ_data = phytotoma[,2:3], mask= buf.M) plot(reduce_cut@cropa$bio1) points(phytotoma[,2:3], pch=16,col='blue')
We need to show correlogram of predictor variables
cor.show(reduce_cut) # Define what variables we need to remove rd <- c('bio1','bio12','bio16','biome','bio8')
Remove Rd
in reduce_cut
, and we have these variables.
cor.show(reduce_cut, rm=TRUE, var.rm = rd)
Define new environmental dataset (no-correlation)
var_reduce <- dropLayer(reduce_cut@cropa, rd) names(var_reduce)
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