knitr::opts_chunk$set(echo = TRUE)
library(wildlifeR) library(ggplot2) library(cowplot) library(ggpubr) library(dplyr)
data(frogarms)
The function make_my_data2L() will extact out a random subset of the data. Change "my.code" to your school email address, minus the "@pitt.edu" or whatever your affiliation is.
my.frogs <- make_my_data2L(dat = frogarms, my.code = "nlb24", # <= change this! cat.var = "sex", n.sample = 20, with.rep = FALSE)
t-test used to tell if two groups are different
t.test(mass ~ sex, data = my.frogs)
spits out R's standard t.test table
Save to object
mass.t <- t.test(mass ~ sex, data = my.frogs)
look at w/broom::glance. relables thiugns a bit odd and would be nice to round. will stick with original R output
library(broom) glance(mass.t)
mass.t
Check the means using dplyr
my.frogs %>% group_by(sex) %>% summarize(mean.mass = mean(mass))
What does all of this mean?
Quiz
p = p interpretation = df = why df fractional? (this one is hard!) t = What would happen to p if t was bigger? What is a "95% CI" What is this a 95% CI for?
What does the CI mean?
plot_t_test_ES(mass.t)
Make a plot of the means with error bars. Save to an object called gg.means
gg.means <-ggerrorplot(data = my.frogs, y = "mass", x = "sex", desc_stat = "mean_ci") + ggtitle("Group means & error bars")
Save effect size
gg.ES <-plot_t_test_ES(mass.t) + ggtitle("__________ & errorbars")
Plot both
plot_grid(gg.means,gg.ES)
A hint
gg.means <-ggerrorplot(data = my.frogs, y = "mass", x = "sex", desc_stat = "mean_ci") + ylab("Mass (g)") + xlab("Sex") + ggtitle("Group means & error bars") gg.ES <-plot_t_test_ES(mass.t) + ylab("Difference between groups (g)") + ggtitle("__________ & errorbars")
plot_grid(gg.means,gg.ES)
Did anyone get a significant result?
(do same thing for arm girth. see the super significant values?)
Now we are going to unpack this
There is a major flaw in this analysis. consider the following graph where the mass is plotted on the x-axis and the arm girth is plottedon the y-axis. Does arm girth vary just because of sex, or becasuse of sex and mass?
This is ANCOVA. Sometimes taught as an extension of ANOVA, or as a type of regression.
ggscatter(data = frogarms, y = "arm", x = "mass", color = "sex")
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