even4void

x <- seq(0,10)
y <- seq(0,10)
df <- expand.grid(x=x, y=y)
df$z <- rnorm(nrow(df), mean=5)

library(lattice)
levelplot(z ~ x+y, data=df, 
          panel=function(x, y, ...) {
            panel.levelplot(x, y, ...)
            panel.abline(v=1)

even4void

@article{arlot2010survey,
	Author = {Arlot, S. and Celisse, A.},
	Journal = {Statistics Surveys},
	Pages = {40--79},
	Title = {A survey of cross-validation procedures for model selection},
	Volume = {4},
	Year = {2010}}

@article{feinerer2008text,
	Author = {Feinerer, I. and Hornik, K. and Meyer, D.},

even4void

display.cell <- function(x, bgcol="#DAE6F2", ...) {
  opar <- par(bg=bgcol, mar=rep(0,4))
  plot(c(0,1), c(0,1), type="n", axes=FALSE, xlab="", ylab="")
  text(.5, .5, as.character(x), ...)
  lines(c(-0.1,1.1), c(0,0))
  par(opar)
}

format.digits <- function(x) as.character(paste("$", as.character(x), sep="   "))

even4void

library(mixOmics)
library(RColorbrewer)

X <- replicate(6, rnorm(50))
X.row.mds <- cmdscale(dist(X), k=1)
X.col.mds <- cmdscale(dist(t(X)), k=1)
cim(cor(X)[order(X.col.mds),order(X.col.mds)], col=rev(brewer.pal(8, "RdBu")))
cim(cor(t(X))[order(X.row.mds),order(X.row.mds)], col=rev(brewer.pal(8, "RdBu")))
cim(X, col=rev(brewer.pal(8, "RdBu"))

even4void

% compile with
%   R CMD Sweave sweave_ggplot.Rnw
%   pdflatex sweave_ggplot.tex
\documentclass[t,ucs,12pt,xcolor=dvipsnames]{beamer}
\usepackage{fancyvrb}

\title{A sample Sweave demo}
\author{Author name}
\date{}

even4void

@article{Narrow2009,
	Author = {Narrow, W.E. and Kuhl, E.A. and Regier, D.A.},
	Journal = {World Psychiatry},
	Month = {Jun},
	Number = {2},
	Pages = {88-89},
	Title = {{DSM-V} perspectives on disentangling disability from
                  clinical significance},
	Volume = {8},
	Year = {2009}}

even4void

tufte.boxplot <- function(x, g) {
  k <- nlevels(g)
  crit.val <- tapply(x, g, median)
  plot(1:k, crit.val, ylim=c(min(x)*1.1, max(x)*1.1), pch=19,
       xlab=deparse(substitute(g)), ylab=deparse(substitute(x)))
  for (i in 1:k) {
    tmp <- boxplot.stats(x[as.numeric(g)==i])
    segments(i, tmp$stats[1], i, tmp$stats[2])
    segments(i, tmp$stats[4], i, tmp$stats[5])
    points(rep(i, length(tmp$out)), tmp$out, cex=.8)

even4void

mha <- 1  # mean under the alternative
es <- 2   # observed effect (deviation from mean under H0)
x <- seq(-6, 6, length=1000)
dh0 <- dnorm(x, 0, 1)

show.it <- function(es, mha, verbose=FALSE) {
  dh1 <- dnorm(x, mha, 1)

  plot.new()
  plot.window(xlim=range(x), ylim=c(0,.6)) 

even4void

# simulate some data
set.seed(88)
n <- 30
x <- runif(n)
y <- 1.2 + 0.8*x + rnorm(n)
g <- sample(letters[1:2], n, replace=TRUE)
my.df <- data.frame(x=x, y=y, grp=factor(g))

# show data structure
dput(my.df)

even4void

#! /usr/bin/env sh

# Post-process Stata do -> log file.

# We should ensure that we use the correct stata program
# (e.g., might be stata, stata-mp, etc.). I'll put this here 
# for later update.
STATA="$(which stata)"

# Process command line options