Paul Hewson phewson
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| shinyServer(function(input, output) { | |
| gameA <- function(p){ | |
| WinLose <- sample(c(1,-1), size = 1, prob=c(p,(1-p))) | |
| } | |
| gameB <- function(CurrentCapital, p1, p2){ | |
| WinLose <- 0 | |
| if ((CurrentCapital %% 3) == 0){ |
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| library(shiny) | |
| # Define server logic required to draw a histogram | |
| shinyServer(function(input, output) { | |
| Data <- reactive({ | |
| # output$contents <- renderTable({ | |
| # input$file1 will be NULL initially. After the user selects | |
| # and uploads a file, it will be a data frame with 'name', |
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| library(arm) | |
| options(warn=-1) | |
| shinyServer(function(input, output){ | |
| Data <- reactive({ | |
| #N <- as.numeric(input$ns) | |
| #prob <- input$prob | |
| lambda <- 0 |
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| library(shiny) | |
| # Define server logic required to generate and plot a random distribution | |
| shinyServer(function(input, output) { | |
| x1 <- c(3.36, 3.24, 5.16, 6.09, 3.08, 6.97, 4.85, 5.42, 5.15, 3.61, 4.95, 2.22) | |
| x2 <- c(2.72, 3.09, 1.49, 1.98, 0.85, 2.56, 2.14, 1.80, 0.57, 3.16, 0.90, 0.15) | |
| x <- c(x1, x2) | |
| mycol = c(rep("red", 12), rep("blue", 12)) | |
| ind <- c(rep(1,12), rep(2,12)) | |
| X <- matrix("numeric", 24, 1001) |
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| library(shiny) | |
| shinyServer(function(input, output) { | |
| Data <- reactive({ | |
| # output$contents <- renderTable({ | |
| # input$file1 will be NULL initially. After the user selects |
phewson
/ LOO.R
Created
October 8, 2013 19:12
This is an animation using the manipulate package in R-studio. It uses the Anscombe data - you get to choose which pair. Then you slide through the datasets and the line is computed omitting that data point, and the leave one out residual is illustrated. I would like to tidy up a few details on this, but it works as a first step.
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| require(MASS) | |
| require(manipulate) | |
| data(anscombe) | |
| plotit <- function(whichpair, i){ | |
| eval(parse(text = paste("sortit <- order(anscombe$x", whichpair, ")", sep = ""))) | |
| eval(parse(text = paste("data.df <- data.frame(x=anscombe$x", whichpair, "[sortit], y=anscombe$y", whichpair, "[sortit])", sep = ""))) | |
| ylims <- c(min(data.df$y)*0.8, max(data.df$y)*1.2) | |
| xlims <- c(min(data.df$x)*0.8, max(data.df$x)*1.2) |
phewson
/ Hamiltonian
Last active
December 19, 2015 04:49
Early explorations in Hamiltonian MC (based on Radford Neal's chapter in the MCMC handbook)
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| { | |
| "metadata": { | |
| "name": "Hamiltonian" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |
phewson
/ gist:5821745
Created
June 20, 2013 10:32
This is a simple illustration of a rejection sampler. At the moment we just get Beta random variables, which is a little unfortunate as we are using two uniform distributions in the simulations which I think confuses a few people.
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| { | |
| "metadata": { | |
| "name": "Rejection Sampler Illustrated" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |
phewson
/ GibbsChangepoint.ipynb
Last active
December 18, 2015 14:29
This is a draft of the Coalmine disasters Poisson changepoint problem, with a Bayesian model estimated by a simple Gibbs sampler written in Python.
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