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This is a page of example models. To learn about installing click here, to learn about path-based model syntax click here.

Three basic methods of modeling supported in OpenMx

• Path analysis
• RAM - LISREL
• Matrix algebra

Confirmatory Factor Analysis

• One factor models
• Multiple factor models

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Resources and guides for online teaching

https://uoe.sharepoint.com/sites/PPLSLearningResources

Sections of this guide are:

1. Meeting, presentation and communication tools (chat/boards etc.)
2. Recording tools
3. FAQ

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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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create.vechsR <- function (A0, S0, F0 = NULL, Ax = NULL, Sx = NULL) {
    if (is.matrix(A0)) {
        A0 = as.mxMatrix(A0, name = "A0")
    }else{
        A0@name = "A0"
    } if (is.matrix(S0)) {
        S0 = as.mxMatrix(S0, name = "S0")
    }else{
        S0@name = "S0"
    }

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List of Topics for OpenMx Workshop

1. Re-Introduction to SEM

• Calculation of Linear Combinations
• Notions of Path Analysis

2. Introduction to the OpenMx Package

• Loading in the Package
• Basic Functions in the Package
• Optimizer choice

3. Univariate and Bivariate Regression

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These code snippets have been tested on R 3.1.0 and Mac OS 10.9.3. They presumably do *not* work on R 2.X!

## Enter these commands in the Mac OS Terminal

# use faster vecLib library
cd /Library/Frameworks/R.framework/Resources/lib
ln -sf  /System/Library/Frameworks/Accelerate.framework/Frameworks/vecLib.framework/Versions/Current/libBLAS.dylib libRblas.dylib

# return to default settings
cd /Library/Frameworks/R.framework/Resources/lib

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# mate ~/.R/Makevars
# The following statements are required to use the clang4 binary
CC=/usr/local/clang4/bin/clang
CXX=/usr/local/clang4/bin/clang++
CXX1X=/usr/local/clang4/bin/clang++
CXX98=/usr/local/clang4/bin/clang++
CXX11=/usr/local/clang4/bin/clang++
CXX14=/usr/local/clang4/bin/clang++
CXX17=/usr/local/clang4/bin/clang++
LDFLAGS=-L/usr/local/clang4/lib

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data(demoOneFactor)
manifests <- names(demoOneFactor)
latents <- c("G1", "G2")

fit2 <- mxRun(mxModel("Two Factor", type="RAM",
      manifestVars = manifests, latentVars = latents,
      mxPath(from = latents[1], to=manifests[1:3]),
      mxPath(from = latents[2], to=manifests[4:5]),
      mxPath(from = manifests, arrows = 2),
      mxPath(from = latents, arrows = 2, free = FALSE, values = 1.0),

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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"
 

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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