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fdabl / variable-selection-comparison.R
Last active Mar 31, 2019
Implements three ways to do Bayesian variable selection. For context, see https://fdabl.github.io/r/Spike-and-Slab.html
View variable-selection-comparison.R
library('doParallel')
registerDoParallel(cores = 4)
#' Spike-and-Slab Regression using Gibbs Sampling for p > 1 predictors
#'
#' @param y: vector of responses
#' @param X: matrix of predictor values
#' @param nr_samples: indicates number of samples drawn
#' @param a1: parameter a1 of Gamma prior on variance sigma2e
@fdabl
fdabl / dirichlet-triangle.R
Last active Dec 5, 2018
Visualizes three dimensional Dirichlet distributions
View dirichlet-triangle.R
# devtools::install_github("dkahle/dirichlet")
library('TeX')
library('ggplot2')
library('gridExtra')
library('dirichlet')
plot_dirichlet <- function(alphas = c(.5, .5, .5), add_points = FALSE) {
f <- function(v) ddirichlet(v, alphas)
mesh <- simplex_mesh(.0025) %>% as.data.frame %>% tbl_df
View ci.R
bfx <- function(x, tstat, n, mu, gamma, k, v = n - 2) {
num <- integrate(function(delta) {
dt(tstat, df = v, ncp = sqrt(n)*delta)/gamma * dnorm((delta - mu)/gamma)
}, -Inf, Inf)$value
dt(tstat, df = v, ncp = sqrt(n)*x) / num
}
lo <- uniroot(function(x) bfx(x, .5, 10, 0, .3) - 1, interval = c(-1, 0))$root
View dynamite.R
# for https://www.facebook.com/efpsa.jeps/posts/1278843852167100
library('ggplot2')
library('gridExtra')
set.seed(1774)
# generate data
A = c(rnorm(20, 20, 4), rnorm(20, 40, 4)) # mixture distribution
B = rt(30, 1) + 30 # from a heavy-tailed distribution (Cauchy)
C = c(25, 35, 33) # just three data points
View bayesian_p2_ANOVA.Rmd
---
title: "Bayesian effect size for ANOVA designs"
author: "Fabian Dablander, Maarten Marsman"
date: "May 22, 2016"
output: html_document
---
## 1) Example: One-way design
Download the data from [https://osf.io/zcipy/](https://osf.io/zcipy/), save it under
"example1.sav" in the current directory.
View unsupervised.jl
function PCA(X)
## Y = PX
# required:
#- P such that
#- we get rid of 2nd order correlation, Cov(Y) = D [some diagonal matrix]
# method:
#- project onto an orthogonal space spanned by the
#- eigenvectors with variances equal to the eigenvalues
View scrape_emails.R
library('rvest')
library('stringr')
fachschaft = 'http://wiki.psyfako.org/index.php?title=Liste_der_Psychologie_Fachschaften'
emails = read_html(fachschaft) %>%
html_nodes('p, a, span') %>% html_text(.) %>%
str_replace('\\[at\\]', '@') %>% str_replace('\\(at\\)', '@') %>%
str_replace('\\[ät\\]', '@') %>% str_replace('\\[dot\\]', '.') %>%
View shiny_regression.R
shinyApp(
options = list(width = '25%', height = '25%'),
ui = shinyUI(fluidPage(
sidebarLayout(
sidebarPanel(
sliderInput('e', label = 'e', min = 0, max = 100, value = 10, step = 1),
sliderInput('N', label = 'N', min = 10, max = 500, value = 100, step = 1),
numericInput('seed', label = 'random seed', value = 1774),
numericInput('lambda', label = 'penalty (lambda)', value = 0),
textInput('true', label = 'true function f(x)', value = 'x^2'),
View bayes_lasso.R
library('runjags')
lasso.bayes <- function(y, X, n.iter = 10000) {
ms <- '
model {
sigma ~ dunif(0, 100)
tau <- pow(sigma, -2)
lambda ~ dunif(0, 10)
View bayesplot.R
# c('practicality', 'difficulty', 'some color, dunno')
papers <- rbind(
# paper we discuss
c(5, 3, 1), # Lindley (1993)
c(8, 0, 1), # Kruschke (2010)
c(4, 3, 1), # Wagenmakers (2007)
c(5, 2, 1), # Dienes (2011)
c(3, 7, 1), # Vandekerckhove, Matzke, Wagenmakers (2015)
c(4, 8, 1), # Rouder & Morey (2012)