In UofTCoders/eeb430.2017.Python: Team Python Final Report for EEB430 at UofT

Load required packages

library(tidyverse)
library(ggplot2)
library(lme4)
library(broom)
library(ggthemes)
library(MuMIn)
library(Matrix)
devtools::load_all(".")

Plot linear model

#library(lme4)
c <- lmer(log.oy ~ log.ml + (log.ml | order), data = mammals_sub)
summary(c)
lme4::ranef(c)
library(broom)
tidy(c, conf.int = TRUE)
broom::augment(c) %>%
    ggplot(aes(x = log.ml, y = .fixed)) +
    geom_line() +
    geom_point(aes(x = log.ml, y = log.oy, color = order), alpha = 0.15) +
    geom_line(aes(y = .fitted, color = order), alpha = 0.8) + 
    team_theme()+
    theme(legend.title = element_blank())+
    labs(x="ln (Maximum Life Span)", y="ln (Offspring per year)")

Table of slopes for each order

Order <- matrix(c((-0.02095551+-0.6788), (-0.55291115+-0.6788),(0.24081831+-0.6788), (0.23992633+-0.6788), (0.21776353+-0.6788), (-0.33380489+-0.6788), (0.20916338+-0.6788)), ncol=1, byrow=TRUE)
colnames(Order) <- c("Slope")
rownames(Order) <- c("Artiodactyla", "Carnivora", "Cetacea", "Insectivora", "Lagomorpha", "Primates", "Rodentia")
Order <- as.table(Order)
Order

Plot the general linear model

base <- glm(log.oy ~ log.ml , data=mammals_sub)
summary(base)
tidy(c, conf.int=TRUE)
broom::augment(base) %>%
    ggplot(aes(x=log.ml, y=.fitted)) +
    geom_line() +
    geom_point(aes(x=log.ml, y=log.oy)) +
    geom_line(aes(y=.fitted)) + 
    team_theme()+
    labs(x="Log (Maximum Life Span)", y="Log (Offspring per year)")