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---
title: "Model Selection"
author: "Jocelyn Stalker"
date: "10/5/2021"
output: html_document
highlight: espresso
---
<body style="background-color:black;">
<style>
div.halloween pre { background-color:#5c1b82; color:#ffffff; }
div.halloween pre.r { background-color:#D35400; }
</style>
<div class = "halloween">
# Reptile Diversity in Agricultural Landscapes
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load packages, import data, include= FALSE}
library(ggplot2)
library(janitor)
library(patchwork)
library(MuMIn)
div.data <- read.csv("~/Documents/ADA_ModelSelection/data.div.csv") %>% clean_names()
```
```{r}
pairs(div.data[,c(3,5:11)], lower.panel = NULL)
```
```{r}
mod1 <- lm(diversity_index ~ ai + human_settlement, data= div.data)
mod2 <- lm(diversity_index ~ agriculture + ai, data= div.data)
mod3 <- lm(diversity_index ~ ai + natural_vegetation, data= div.data)
mod4 <- lm(diversity_index ~ natural_vegetation + seminatural_vegetation, data= div.data)
mod5 <- lm(diversity_index ~ agriculture + human_settlement, data= div.data)
```
```{r model selection}
mod.sel <- model.sel(mod1,mod2,mod3,mod4,mod5)
mod.sel
```
```{r}
subset(mod.sel, cumsum(mod.sel$weight) <= .95)
```
```{r}
# Consistent AIC with Fishers information matrix
model.sel(mod1,mod2,mod3,mod4,mod5, rank = CAICF)
```
```{r importance}
importance(mod.sel)
```
```{r Model Averaging}
# Model average using all candidate models, always use revised.var = TRUE
mod.avg.ests <- model.avg(mod.sel, revised.var = TRUE)
mod.avg.ests
```
```{r model 1}
anova(mod1)
```
```{r make a figure for funsies}
f1 <- ggplot(div.data, aes(ai, diversity_index)) +
geom_point() +
geom_smooth(method="lm") + labs(title= "Reptile Diversity as a Function of \n Agricultural Intensity", x= "Agricultural Intensity", y= "Reptile Diversity Index")
f2 <- ggplot(div.data, aes(human_settlement, diversity_index)) +
geom_point() +
geom_smooth(method="lm") + labs(title= "Reptile Diversity as a Function of \n Nearby Human Settlements", x= "Human Settlements", y= "Reptile Diversity Index")
f1+f2
```
# Drivers of Density of an Urban Lizard
```{r import data, include= FALSE}
lagartixas <- read.csv("~/Documents/ADA_ModelSelection/lagartixas.csv") %>% clean_names()
```
```{r check for colinearity}
pairs(lagartixas[,9:13], lower.panel = NULL)
# arthropods and herb cover look suspicious, so let's not include them in the same model
```
```{r build models}
m1 <- lm(encounter_rate_lagartixas_ind_m ~ trees_m + mean_herbcover, data= lagartixas)
m2 <- lm(encounter_rate_lagartixas_ind_m ~ mean_impervious + mean_shelters, data= lagartixas)
m3 <- lm(encounter_rate_lagartixas_ind_m ~ mean_arthropods + mean_shelters, data= lagartixas)
m4 <- lm(encounter_rate_lagartixas_ind_m ~ trees_m + mean_arthropods, data= lagartixas)
m5 <- lm(encounter_rate_lagartixas_ind_m ~ mean_impervious + mean_herbcover, data= lagartixas)
```
```{r model selection 2}
mod.sel2 <- model.sel(m1,m2,m3,m4,m5)
mod.sel2
```
```{r build another model, run model selection}
m6 <- lm(encounter_rate_lagartixas_ind_m ~ mean_shelters + mean_impervious + mean_arthropods, data= lagartixas)
m7 <- lm(encounter_rate_lagartixas_ind_m ~ mean_shelters + mean_impervious + mean_arthropods + trees_m, data= lagartixas)
mod.sel3 <- model.sel(m1,m2,m3,m4,m5,m6,m7)
mod.sel3
```
```{r subsetting models}
subset(mod.sel3, delta <2)
```
```{r analyze}
anova(m3)
anova(m2)
anova(m7)
```
```{r}
f3 <- ggplot(lagartixas, aes(mean_shelters, encounter_rate_lagartixas_ind_m)) +
geom_point() +
geom_smooth(method="lm") + labs(title= "The Effect of Shelters on \nLagartixa Encounter Rate", x= "Mean # of Shelters", y= "Lagartixa Encounter Rate (individuals/meter)")
f4 <- ggplot(lagartixas, aes(trees_m, encounter_rate_lagartixas_ind_m)) +
geom_point() +
geom_smooth(method="lm") + labs(title= "The Effect of Trees on \nLagartixa Encounter Rate", x= "Trees per Meter", y= "Lagartixa Encounter Rate (individuals/meter)")
f3+f4
```