CC-8-Modelling/Modelling_script.R at master · awl75/CC-8-Modelling · GitHub

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############################################################
#                                                          #
#                Coding Club 01-03-2017                    #
# Practicing modelling using  different data distributions #
#                                                          #
############################################################


# This is a sample script outlining the different tasks for this tutorial
# Edit this script to add in your own code

# Libraries ----
library(lme4)
library(ggplot2)

# Defining a ggplot2 theme we will use for plotting later
theme.clean <- function(){
  theme_bw()+
    theme(axis.text.x=element_text(size=12, angle=45, vjust=1, hjust=1),
          axis.text.y=element_text(size=12),
          axis.title.x=element_text(size=14, face="plain"),
          axis.title.y=element_text(size=14, face="plain"),
          panel.grid.major.x=element_blank(),
          panel.grid.minor.x=element_blank(),
          panel.grid.minor.y=element_blank(),
          panel.grid.major.y=element_blank(),
          plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), units = , "cm"),
          plot.title = element_text(size=20, vjust=1, hjust=0.5),
          legend.text = element_text(size=12, face="italic"),
          legend.title = element_blank(),
          legend.position=c(0.9, 0.9))
}

# Load data ----

# For a simple linear model

install.packages("agridat")
library(agridat)

# Using a sample dataset from the agridat package

apples <- agridat::archbold.apple

apples$spacing2 <- as.factor(apples$spacing)

# visualize data with a box plot

apples.p <- ggplot(apples, aes(spacing2, yield)) +
    geom_boxplot(fill = "#CD3333", alpha = 0.8, colour = "#8B2323") +
    theme.clean() +
    theme(axis.text.x = element_text(size = 12, angle = 0)) +
    labs(x = "Spacing (m)", y = "Yield (kg)")

# test lm

apples.m <- lm(yield ~ spacing2, data = apples)
summary(apples.m)

# test lm again

sheep <- agridat::ilri.sheep   # load the data

library(dplyr)
sheep <- filter(sheep, ewegen == "R")   # there are confounding variables in this dataset that we don't want to take into account. We'll only consider lambs that come from mothers belonging to the breed "R".

head(sheep)  # overview of the data; we'll focus on weanwt (wean weight) and weanage

sheep.m1 <- lm(weanwt ~ weanage, data = sheep)   # run the model
summary(sheep.m1)                                # study the output


# Using population trend data for the European Shag from the Living Planet Index
# Import the shagLPI.csv file from the project's directory


# Using a dataset on weevil damage to Scott's pine
# Import the Weevil_damage.csv file from the project's directory

# A simple linear model ----


# Generalised linear models ----

# Poisson distribution

# Binomial distribution