dsparks

SmoothCoefficientPlot <- function(models, modelnames = "", removeintercept = FALSE){
  # models must be a list()

  Alphas <- seq(1, 99, 2) / 100

  Multiplier <- qnorm(1 - Alphas / 2)
  zzTransparency <<- 1/(length(Multiplier)/4)
  CoefficientTables <- lapply(models, function(x){summary(x)$coef})
  TableRows <- unlist(lapply(CoefficientTables, nrow))

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

wadey

# Usage:
# source iterm2.zsh

# iTerm2 tab color commands
# https://iterm2.com/documentation-escape-codes.html

if [[ -n "$ITERM_SESSION_ID" ]]; then
    tab-color() {
        echo -ne "\033]6;1;bg;red;brightness;$1\a"
        echo -ne "\033]6;1;bg;green;brightness;$2\a"

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.

andyferra

body {
  font-family: Helvetica, arial, sans-serif;
  font-size: 14px;
  line-height: 1.6;
  padding-top: 10px;
  padding-bottom: 10px;
  background-color: white;
  padding: 30px; }

body > *:first-child {

VikParuchuri

term<-c("egypt","jordan","israel","saudi")
term_vec<-foreach(i=1:length(all_score_frames),.combine=rbind) %do%
{
  score_row<-rep(0,length(term))
  for(z in 1:length(score_row))
  {
    sel_score<-all_score_frames[[i]][all_score_frames[[i]]$term==term[z],"score"]
    sel_score[is.na(sel_score)]<-0
    if(length(sel_score)==0)
      sel_score<-0

dsparks

# Randomly allocating observations into groups, for, e.g. cross-validation
kk <- 10  # Number of partitions, as in "kk-fold cross-validation."

# Here is a data.frame full of good data:
nn <- 1003
myData <- data.frame(matrix(rnorm(nn * 3), ncol = 3))
colnames(myData) <- LETTERS[1:3]

# This does not work:
whichK <- sample(LETTERS[1:kk], nrow(myData), replace = T)

dsparks

# A simple approach to visually-weighted regression plots

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2", "reshape2", "MASS")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate some data:
nn <- 1000
myData <- data.frame(X = rnorm(nn),