2024

Load data, remove NAs

## Libraries
library(vegan)

## Loading required package: permute

## Loading required package: lattice

## This is vegan 2.6-6.1

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(MuMIn)
library(car) 

## Loading required package: carData
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## Attaching package: 'car'
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library(lme4)

## Loading required package: Matrix
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library(lmerTest)  

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library(performance) 
library(glmmTMB) 
library(fitdistrplus) 

## Loading required package: MASS
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## Loading required package: survival

library(logspline)
library(pwr)
library(Hmisc)

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## Load data
data <- read.csv("NewData8.csv", stringsAsFactors = FALSE)
data[data == "#VALUE!"] <- NA ## convert Excel errors to NA
data[,15:22] <- apply(data[,15:22], 2, as.numeric)
data$AgeClass <- as.factor(data$AgeClass) ## convert ageclass to factor


## Select response variables
responseVars <- c("BlueLUM","BlueBY","BlueRG","RedLUM","RedBY", "RedRG")


## Select predictor variables
predictorVars<- c( "Age", "Prevalence", "Richness", "GCMEAN", "ARMEAN", "Ordinal")


#Drop AgeClass
data2 <- data[ -c(4) ]

## Drop NA

data6 <- na.omit(data2)

##Load Sociosexual Data

data_socio <- read.csv("Data_counts_9Aug.csv", stringsAsFactors = FALSE)

Intermale Analyses