JonasMoss

## This script uses the tmvtnorm package, check it out.

if(!("tmvtnorm" %in% rownames(installed.packages()))) {
  install.packages("tmvtnorm")
}

library("tmvtnorm")

## Seed for reproducibility.
set.seed(313)

JonasMoss

## Needed to calculate moments.
library("moments")
set.seed(313)

## An illustration with both variables skewed.
n = 200
x = rbeta(n, 1, 15)
y = 3 + x + rnorm(n, 0, 0.01)

## Plotting!

JonasMoss

JonasMoss

N        = 1000
K        = 1
res      = MCMCpack::rdirichlet(N, K*c(0.1, 0.15, 0.7, 0.05))
score    = rowSums(res^2)

trust    = res%*%c(-1,-1,1,0) + rnorm(N, 0, 0.1)
covs     = data.frame(afro = res[1,], latios = res[2,], whites = res[3,], asians = res[4,], score = score)

lm(trust ~ ., data = covs)

JonasMoss

## Makes a plot checking for clear associations between politics and postign in SSC.
## Data from: http://slatestarcodex.com/Stuff/ssc2018public.xlsx

library("tidyverse")
SSC = read_excel("ssc2018public.xlsx") # Downloaded from SSC.

Politics = as.factor(SSC$PoliticalAffiliation)
Comments = as.factor(SSC$Comment)

## Not everyone will agree with my classification. 

JonasMoss

## Checks for associations between politics and posting at SSC, using Beta-Bayes!
## Data from: http://slatestarcodex.com/Stuff/ssc2018public.xlsx

library("tidyverse")
SSC = read_excel("ssc2018public.xlsx") # Downloaded from SSC.

Politics = as.factor(SSC$PoliticalAffiliation)
Comments = as.factor(SSC$Comment)

## Not everyone will agree with my classification. 

JonasMoss

## Make a function rkde that samples from a kernel density

#' Sample from a kernel density.
#' 
#' @param n Number of observations to sample.
#' @param x The data from which the estimate is to be computed.
#' @param bw Desired bandwidth.
#' @return A numeric vector with n sampled data points from the kernel
#' density estimator.

JonasMoss

## Functor example 1: Memoization. 
## We memoize a function g with up to N values.

## The function to memoize: It is slow to calculate, so we'll gain from 
## storing its values.

g = function(x) {
    Sys.sleep(0.1)
    print("Processesing ...")
    x^2

JonasMoss

## A simple example of a functor in R.

## A simple function of one argument. Since y is neither defined inside the function body or
## given as an argument, R will search the enclosing environment for y. Since R is lazy, it 
## won't do this when the function is defined, but only when the function is called (and y
## is needed.)
f = function(x) x^2 + y^2

## This changes the enclosing environment of f to a new environment. It was the global
## environment before, and we don't want that. 

JonasMoss

## These are generic functions for extracting and assigning values to the enclosing
## environment of a function. It needs class(f) = "function" to work.

`$.function` = function(f, y) environment(f)[[y]]

`$<-.function` = function(f, y, value) {
  environment(f)[[y]] = value
  invisible(f)
}