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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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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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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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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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library("umx")
con <- url("https://www.dropbox.com/s/r9e3om73viquzfq/disc.rda?raw=1")
print(load(con)); close(con)
manifests = c("q1", "q2", "q3", "q4", "q5", "q6", "q7")
latents <- c("F1", "F2") # 2 factors

m1 <- mxModel("twoFactors", type="RAM", manifestVars = manifests, latentVars = latents,
	mxPath("F1", to = manifests),
 	mxPath("F2", to = manifests),
 	mxPath(latents, arrows = 2, free = FALSE, values = 1),

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# ============================
# = How heritable is height? =
# ============================
require(umx)

data(twinData) # ?twinData from Australian twins.
# Pick the variables
selDVs = c("ht1", "ht2")
mzData <- twinData[twinData$zygosity %in% "MZFF", ]
dzData <- twinData[twinData$zygosity %in% "DZFF", ]

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Haimovitz, K., & Dweck, C. S. (2016). What Predicts Children's Fixed and Growth Intelligence Mind-Sets? Not Their Parents' Views of Intelligence but Their Parents' Views of Failure. Psychological Science. doi:10.1177/0956797616639727

    
require(umx)
data = sem::readMoments(file = "", diag = TRUE)
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