tbates

n = 1000; 
coins = rep(0, n)
for (i in 2:n) {
    whatToFlip = seq(from = i, to = n, by = i)
    coins[whatToFlip] = coins[whatToFlip] + 1
}
sqrt(which(!as.logical(coins%%2)))
 # [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

tbates

require(lavaan)
 bifactor <- "
 general.factor =~ Easy_Reservation + Preferred_Seats + Flight_Options + Ticket_Prices 
				+ Seat_Comfort + Seat_Roominess + Overhead_Storage
				
				+ Clean_Aircraft + Courtesy + Friendliness + Helpfulness + Service
 ticketing =~ Easy_Reservation + Preferred_Seats + Flight_Options + Ticket_Prices
 aircraft  =~ Seat_Comfort + Seat_Roominess + Overhead_Storage + Clean_Aircraft
 service   =~ Courtesy + Friendliness + Helpfulness + Service"
 

tbates

library(MASS)
library(ggplot2)

# move n around to alter sample size
# move r around to alter effect size
n = 1000;  r = .5
desiredCovMatrix = matrix(c(1,r,r, 1) ,nrow=2, ncol=2);
count = 1000 # number of replications
out = rep(NA,count) # array to store the results
for (i in 1:count) {

tbates

#!/usr/bin/ruby
VERSION = 1.3

require 'net/https'
require 'rubygems'
require 'json'
require 'cgi'

# set to true to force inline links
inline = false

tbates

umxSaturated <- function(m1, evaluate = T) {
	# Use case
	# m1_sat = umxSaturated(m1)
	# summary(m1, SaturatedLikelihood=m1_sat$SaturatedLikelihood, IndependenceLikelihood=m1_sat$IndependenceLikelihood)
	manifests           = m1@manifestVars
	nVar                = length(manifests)
	theData             = m1@data@observed
	dataMeans           = colMeans(theData)
	meansLabels         = paste("mean", 1:nVar, sep="")
	loadingsLabels      = paste("F", 1:nVar, "loading", sep="")

tbates

setMethod("imxVerifyModel", "MxRAMModel",
	function(model) {
		if ((length(model$A) == 0) ||
			(length(model$S) == 0) ||
			(length(model$F) == 0)) {
				msg <- paste("The RAM model", omxQuotes(model@name),
                "does not contain any paths.",
				" Are you just starting out? you need to add paths like",

tbates

	if (identical(options[["Standard Errors"]], "Yes") &&
		identical(options[["Calculate Hessian"]], "No")) {
		msg <- paste('The "Standard Errors" option is enabled and',
			'the "Calculate Hessian" option is disabled. Generating',
			'standard errors requires the Hessian calculation. Please',
			'disable standard errors or enable the Hessian calculation.\n',
			'You can do this with\n', 
			'model <- mxOption(model, "Standard Errors", "No")\n',
			'or\n', 
			'model <- mxOption(model, "Calculate Hessian", "Yes")'

tbates

persons = rnorm(100, 20,10)
nYearsToRun = 500
births = deaths = pop = rep(NA,nYearsToRun)


for (y in 1:nYearsToRun) {
	populationSize = length(persons)
	persons = persons+1 # age everybody
	# death
	persons <- persons[persons < rnorm(populationSize, 77, 15)]

tbates

# Above are some results from the a matrix of a 
# trivariate cholesky with the above diag cells filled in

A = matrix(nrow = 3, byrow = T, c(.71, -.28, .15, -.28, .61, -.38, .15, -.38, .000001))
      [,1]  [,2]     [,3]
[1,]  0.71 -0.28  1.5e-01
[2,] -0.28  0.61 -3.8e-01
[3,]  0.15 -0.38  1.0e-06

# Yeah... so that's wrong. that's the lower a matrix.

tbates

x <- data.frame(matrix(ncol = 2, byrow = T, c(
	-0.7,  0.2,
	2.1,  2.7,
	1.7,  2.3,
	1.4,  0.8,
	1.9,  2.0,
	1.8,  1.0,
	0.4, -0.4,
	1.1,  0.3,
	0.9,  0.4,