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# umxRun now detects raw RAM, and runs sat and ind. worth speeding this bit up a bit?
m3 <- mxModel("independence",
    # TODO: slightly inefficient, as this has an analytic solution
    mxMatrix(name = "variableLoadings" , type="Diag", nrow = nVar, ncol = nVar, free=T, values = independenceStarts), 
	# labels = loadingsLabels),
    mxAlgebra(name = "expCov", expression = variableLoadings %*% t(variableLoadings)),
    mxMatrix(name  = "expMean", type = "Full", nrow = 1, ncol = nVar, values = dataMeans, free = T, labels = meansLabels),
    mxFIMLObjective(covariance = "expCov", means = "expMean", dimnames = manifests),
    mxData(theData, type = "raw")
)

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| | Grouping ||

First Header	Second Header	Third Header
Content	Long Cell
Content	Cell	Cell

| New section | More | Data | | And more | And more || [ Table Caption ]

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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,

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# 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.

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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)]

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	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")'

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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",

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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="")

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#!/usr/bin/ruby
VERSION = 1.3

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

# set to true to force inline links
inline = false

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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) {