even4void

cronbach.alpha <- function(data, na.action="omit", signif.level=.05) {
  # computes Cronbach's Alpha
  # * Input *
  #    data         : a n*k matrix, where each column hold one item responses'
  #                   vector
  #    na.action    : if "omit", then handle missing value in cov/var
  #                   calculation [default]
  #    signif.level : type I error
  # * Output *
  #    a list with statistic and confidence interval

even4void

import graph;
import patterns;

size(300,200,IgnoreAspect);

real xmin=-4,xmax=4;
real ymin=0,ymax=1;
real PI=3.141593;
real sd=.6;
int a=-1, b=1;

even4void

import graph;
import patterns;

size(300,200,IgnoreAspect);

real xmin=-4,xmax=4;
real ymin=0,ymax=1;
real PI=3.141593;
real sd=.6;
int a=-1, b=1;

even4void

import graph;
import patterns;

size(300,200,IgnoreAspect);

real xmin=-4,xmax=4;
real ymin=0,ymax=1;
real PI=3.141593;
real sd=.6;
real a=1, b=-.5;

even4void

# Some illustrations of reliability analysis.
# Time-stamp: <2010-10-17 21:35:51 chl>
x <- c(12,8,22,10,10,6,8,4,14,6,2,22,12,7,24,14,8,4,5,6,14,5,5,16)
GHQ <- data.frame(subject=gl(12,1,24), time=gl(2,12), score=x)

# ----------------------------------------------------
# Computing reliability (Rx) by hand from ANOVA tables 
# ----------------------------------------------------
# one-way ANOVA (assuming no time trends)
summary(aov(score ~ subject, data=GHQ))

even4void

itan <- function(x, keys=NULL, digits=3, no.resp=4) {
  # x is an n subjects by k items matrix
  # keys is a vector with the correct keys (A, B, C, D)
  stopifnot(ncol(x)>1)
  if (is.null(keys)) keys <- rep("A", ncol(x))
  require(ltm)
  raw.resp <- matrix(nr=ncol(x), nc=no.resp)
  colnames(raw.resp) <- LETTERS[1:no.resp]
  for (i in 1:ncol(x)) {
    tmp <- table(x[,i])

even4void

# Some examples of graphical displays for Likert items
#
# Time-stamp: <2010-10-24 20:04:59 chl>
data(Environment, package="ltm")
Environment[sample(1:nrow(Environment), 10),1] <- NA
na.count <- apply(Environment, 2, function(x) sum(is.na(x)))
tab <- apply(Environment, 2, table)/apply(apply(Environment, 2, table), 2, sum)*100

# Cleveland dot plot
dotchart(tab, xlim=c(0,100), xlab="Frequency (%)", sub=paste("N", nrow(Environment), sep="="), pch=19)

even4void

# Time-stamp: <2010-12-10 14:28:19 chl>

#
# A toy example to illustrate SS idea in the Linear Model.
#

do.it <- function(x, g) {
  stopifnot(is.factor(g))
  stripchart(x ~ g, method="jitter", pch=19, col="darkgrey",
                        vertical=TRUE, ylab="")

even4void

prologues := 2;

input geometriesyr16;

figure(-u,-u,6.5u,6u);

pair A,B,C,D,E,F,G,H,I,J,K,L,M,N;
picture depart;
numeric i,j,r,v;
r:=.5;

even4void

# Time-stamp: <2011-01-07 12:41:47 chl>

#
# Some illustrations of SQL joins with R.
# Inspiration: Jeff Atwood, http://bit.ly/eMhJEp
#

tableA <- data.frame(id=1:4, name=c("Pirate","Monkey","Ninja","Spaghetti"))
tableB <- data.frame(id=1:4, name=c("Rutabaga","Pirate","Darth Vader","Ninja"))