betterKUenm: An R package for basic niche modeling analysis

Marlon E. Cobos, Ben Wiens, Daniel Rojas, Andres Herrera, Rahul Raveendran, Weverton, Eric Ng’eno, Felipe Arias

• Package description
• Installing the package
  • Functions in ellipsenm
• ext_back function: Extract background data from variables
• fitGLM function: Fits Generalized Linear Models for only presences data
• occ_prep function: Extract variables in coordinates
• projectionGLRM function: Project GLMs to Raster Variables
• R Markdown
• Including Plots

This repository is for the project “betterKUenm” developed during Marlon’s workshop Spring 2023.

Package description

The betterKUenm R package implements basic tools for ecological niche modeling of species.

Installing the package

*betterKUenm** is in a GitHub repository and can be installed and/or loaded using the code below (make sure to have Internet connection). One of the functions to evaluate model performance in this package needs compilation. That is why you must install a compilation tools before installing the package, Rtools for Windows or other tools in other Operative Systems. A guide for downloading and installing Rtools can be found here. IMPORTANT note: Add Rtools to the **PATH** during its installation.

Try the code below first… If you have any problem during the installation, restart your R session, close other sessions you may have open, and try again. If during the installation you are asked to update packages, please do it (select the option that says All). If any of the packages gives an error, please install it alone using install.packages(), then try re-installing betterKUenm again. Also, it may be a good idea to update R and RStudio (if you are using it).

# Installing and loading packages
if(!require(devtools)){
    install.packages("devtools")
}
if(!require(remotes)){
    install.package("remotes")  
}
if(!require(betterKUenm)){
    devtools::install_github("omys-omics/betterKUenm")
}
library(betterKUenm)

Functions in ellipsenm

A complete list of the main functions in the betterKUenm package can be found in the package documentation. Use the following code to see the list.

help(betterKUenm)

ext_back function: Extract background data from variables

ext_back mask the environmental variables and extract the values of n cells at random

We encourage the users to check the function’s help before using it. This is possible using the code below:

help(betterKUenm_ext_back)

fitGLM function: Fits Generalized Linear Models for only presences data

We encourage the users to check the function’s help before using it. This is possible using the code below:

help(betterKUenm_fitGLM)

occ_prep function: Extract variables in coordinates

Extract variables in coordinates

We encourage the users to check the function’s help before using it. This is possible using the code below:

help(betterKUenm_occ_prep)

projectionGLRM function: Project GLMs to Raster Variables

We encourage the users to check the function’s help before using it. This is possible using the code below:

help(betterKUenm_occ_prep)

R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.