jonesor

par(mar=c(1,1,1,1))
plot(x = rep(1:5,5),y = rep(1:5,each=5),pch=1:25,col="black",bg="yellow",axes=F,xlab="",ylab="",
     cex=2,xlim=c(0,5.5),ylim=c(0,5.5))
text(x = rep(1:5,5),y = rep(1:5,each=5),text=1:25,pos=2,cex=.8)

jonesor

#bar heights
x<- c(1,2,3,2,4)

#standard error values
sem <- c(.5,.4,.2,.4,.2)

#bar labels

lab <- c("A","B","C","D","E")

jonesor

#How to subset the matrix database based on the matrices themselves.
#This example counts NA values in the F matrix and uses that to subset.
nmat <- nrow(compadre$metadata)
for(i in 1:nmat){
  compadre$metadata$NAinFmat[i] <- sum(is.na(compadre$mat[[i]]$matF))  
}

table(compadre$metadata$NAinFmat)

x <- Rcompadre::subsetDB(compadre,NAinFmat == 0)

jonesor

#offsets
#Simulate some data for this example
set.seed(12)
sig <- matrix(c(1.1,1,1,1.1),2,2)
df1<-data.frame(MASS::mvrnorm(n = 100, c(30,30), Sigma = sig ))
names(df1) <- c("x","y")

#Plot the data, and a 1:1 line
plot(df1$x,df1$y)
abline(0,1)

jonesor

#Packages
library(ggplot2)

#Read in some data for the example
#THe data are grade data and acceptance in to grad school.
#GRE (Graduate Record Exam scores), GPA (grade point average) 
mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
head(mydata)
summary(mydata)
mylogit <- glm(admit ~ gre + gpa + gre:gpa, data = mydata, family = "binomial")

jonesor

#I first simulate a dataset to use for this example
set.seed(123)
years <- 2013:2015
n <- 100
df1 <- data.frame(year = rep(years, each = n),
                  eggdate = c(rnorm(n, 40, 4),
                              rnorm(n, 45, 5),
                              rnorm(n, 67, 7)))

#Make sure year is coded as a "factor"

jonesor

# de Finetti diagram
library(dplyr)
library(ggplot2)
library(ggtern)
library(magrittr)

HWEfrequencies <- data.frame(
  p = seq(0, 1, 0.01)
) %>%
  mutate(q = 1 - p) %>%