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Models

1. Structural Equation Model

• RAM or LISREL model where the entries of A and S are numeric

2. Extended SEM

• Model in which the RAM equivalent of A and S entries may be arbitrary algebras.

3. Definition variable

• Any value in an SEM that is replaced with a person-specific value such that it is different for every row or person.

4. Multilevel model

• Relational SEM (by analogy to the way in which a relational database links values in different tables. How OpenMx implements mulit-level

5. Item Response Theory

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# ================================================================ #
# = Task: Write a 1-paragraph summary of your thesis experiment,   #
# = first in 1st person active, then in 3rd person passive         #
# ================================================================ #

# Rated objectivity of text
objectivity1stPerson = c(7, 6.5, 7.2, 6, 6);
objectivity3rdPerson = c(9, 8.5, 8.0, 8, 8)
t.test(one, two, paired= TRUE, alt= "less")
# Mean difference: -1.76 [-Inf -1.25] (t(4) = -7.3, p = <.001)

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# Updated for umx 1.7+ 2017-06-12 04:28PM
# Notes: If you're on Mac or Unix, install the parallelOpenMx to get parallel (4x speedup or more)
# source('http://openmx.psyc.virginia.edu/getOpenMx.R')
library(umx)
umx_set_optimizer("NPSOL") # good optimizer for these data
umx_set_cores(detectCores()) # Max cores for speed

nSimulations = 1000                  # Number of simulations
nMZpairs     = nDZpairs = 500        # Number of twin pairs
pvalues      = rep(NA, nSimulations) # placeholder for the p-values from mxCompare-ing the 2 models you are testing

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library(ggplot2)
library(scales)
gal_per_l = 0.219969
mi_per_km = 0.621371

l100km     = seq(2, 13, by = .1)
l_per_km   = l100km/100
l_per_mi   = l_per_km/mi_per_km
gal_per_mi = l_per_mi * gal_per_l
mpg        = 1/gal_per_mi

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if (mxOption(NULL, "Default optimizer") == 'NLOPT') stop("SKIP")
library("devtools"); library("ggplot2"); 
# library("umx")
# library("umx.twin")
devtools::document("~/bin/umx"); devtools::install("~/bin/umx"); 
devtools::document("~/bin/umx.twin"); devtools::install("~/bin/umx.twin");
demand("formula.tools") # for residualizing inline

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Package existing teaching as TA ships

http://www.jobs.ac.uk/job/ATU280/graduate-teaching-assistant-x2-psychological-sciences

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The Difference Pool

1. Subjects: Population sample

• N must be over 500, preferably 2,000
• Ideally genetically informative

6. Recruitment

• Students
• Public advertisements

2. Content: There will be a set of basic features that will be tested at the outset

• Need to compromise on this. Perhaps 2-hours of testing in the first instance?

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require(OpenMx)
data(demoOneFactor)
manifests <- names(demoOneFactor)
latents <- c("G")
m1 <- mxModel("One Factor", type="RAM",
	manifestVars = manifests,
	latentVars = latents,
	mxPath(from=latents, to=manifests),
	mxPath(from=manifests, arrows=2),
	mxPath(from=latents, arrows=2,

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data(myFADataRaw, package = "OpenMx")
latents = c("G")
manifests = names(myFADataRaw)
myFADataRaw = myFADataRaw[, manifests]
m1 <- mxModel("m1", type="RAM", manifestVars = manifests, latentVars = latents,
	mxPath(from = latents, to = manifests, values = 0),
	mxPath(from = manifests, arrows = 2), # manifest residuals 
	mxPath(from = latents, arrows = 2, free = F, values = 1), # latents fixed@1
	mxData(cov(myFADataRaw, use = "complete"), type = "cov", numObs = nrow(myFADataRaw))
)

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Bun&Patty Burgers in Brioche buns: Serves 4

Source: Waitrose Kitchen Magazine, May 2014: Food & Sport Special (page 49) by Pippa Middleton

Ingredients

1. Burger
   • 500g beef mince, 20% fat
   • 4 slices red leicester
   • Lettuce