ptoche

tslumley

moe = function(pct, N=1000, deff=c(1,2)){
	p.L = function(x, n) {
        ifelse (x == 0,0,qbeta(0.025, x, n - x + 1))
    }
    p.U = function(x, n) {
        ifelse(x == n,1,qbeta(0.975, x + 1, n - x))
    }
    N = rep(N,length(pct))
		
	lower = function(pct,deff){

hadley

a1 <- c(4.2,3.2,11.1,1.3,2.2,2.0)
a2 <- c(3.9,3.2,10.0,0.8,3.1,3.1)
a3 <- c(6.3,2.5,9.8,0.9,2.2,2.4)
a4 <- c(4.4,3.1,9.8,0.8,3.3,2.7)
a5 <- c(4.8,3.0,9.9,0.7,3.3,2.4)
a6 <- c(4.0,3.4,10.5,0.7,3.3,2.1)
a <- rbind(a1,a2,a3,a4,a5,a6)

# Get data into tidy format, starting with a, defined above

jbryer

source('https://raw.github.com/gist/1606595/269d61dfcc7930f5275a212e11f3c43771ab2591/GoogleReader.R')

rbloggers = getRSSFeed(feedURL="http://r-bloggers.com/feed",
			   email="GOOGLE READER EMAIL",
			   passwd="GOOGLE READER PASSWORD",
			   posts=5000)
entries = rbloggers[which(names(rbloggers) == "entry")]
length(entries)
saveXML(rbloggers, file='rbloggers.xml')

thomasjensen

##read in the libraries and set the working directory
library(tm)
library(corrplot)
setwd("/path/to/")

##read in the data and subset it to the relevant categories
data <- read.csv("indvandringPolitikken.csv", fileEncoding = "latin1")
data <- data[data$kategori == "Politik" | data$kategori == "Debat" | data$kategori == "Kronikken" | data$kategori == "Leder", ]

##create the corpus and clean it

alexstorer

function h = plotDecision(dprime,sdnoise,criterion)
% h = plotDecision(dprime, sdnoise, criterion)
%
% plotDecision makes a plot of two Gaussian functions, inspired by signal
% detection theory.  The inputs are given in terms of standard deviates of
% the signal distribution:
%
% dprime    - d', the distance between the means of the Gaussians
% sdnoise   - $\sigma$, the ratio of the standard deviation of the noise to
%             the standard deviation of the signal.

cdesante

###### Ordered Categorical DV: #####
#This uses "importance of aid to blacks" as a DV and race of R as the indicator IV.#
     library(MASS)
     white <- c(1,1,0,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,
1,1,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,0,1,1,1,1,0,1,0,0,1,0,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,
1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,0,0,1,1,1,1,1,1,1,0,1,0,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,
0,1,1,0,1,1,1,1,1,1,0,0,1,1,0,1,0,1,1,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,0,1,0,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,
1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,

christophergandrud

#################
# Use R to recreate part of QMV the relative non-proportional hazard estimation graph (fig 2) from Licht (2011) 
# Christopher Gandrud (http://christophergandrud.blogspot.com/)
# 4 October 2012
#################


#### Set Up ####

# Load required libraries

chrishanretty

library(gdata)
library(countrycode)

infile <- "IMFmultipliers.xls"
imf_rep <- read.xls(infile, sheet = 2,
	skip = 3,
	header=F)

names(imf_rep) <- c("Country",
	"gdp_4cast_2011","gdp_4cast_2012",

chrishanretty

library(plyr)
library(zoo)

startyr <- 1992
endyr <- 2012

pg <- read.csv("http://www.parlgov.org/stable/static/data/stable-utf-8/view_cabinet.csv",as.is=T)

## Exclude GDR
pg <- pg[which(pg$country_name_short!="GDR"),]