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
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
|
require(OpenMx)
require(MASS)
# Seed
set.seed(4)
# Data covariance matrix
dataCov <- rbind(c(4,3,2,1),
c(3,4,3,2),
c(2,3,4,3),
c(1,2,3,4))
dataMean <- c(2,-2,1,-1)
# Variable names
manifests <- c('X','Y','Z','W')
latents <- c('G', 'H')
# Dimnames
colnames(dataCov) <- manifests
rownames(dataCov) <- manifests
names(dataMean) <- manifests
factorModel <- mxModel("Factor Model 2L4M",
type="RAM",
manifestVars = manifests,
latentVars = latents,
# Regression Loadings
mxPath(from='G', to=manifests,value=c(1,2,3,4),free=FALSE),
mxPath(from='H', to=manifests,value=c(1,1,1,1),free=FALSE),
# Variances
mxPath(from=manifests, arrows=2,value=c(1,1,1,1), labels=c('E1','E1','E1','E1')), # error variance
mxPath(from=latents, arrows=2,values=1.0, labels=c("VG", "VH")), # variances of the latents
# Latent means vector
mxPath(from="one", to=latents, arrows=1, values=0, labels=c("MG", "MH"), free=TRUE),
mxData(dataCov, mean=dataMean, type="cov",numObs=5)
)
# Latent Covariance Path
satPath <- mxPath(from='G', to='H',labels="Cov", arrows=2,values=0.1) # Saturated (covariance between the latents)
for (fixALatent in 0:1)
{
testModel <- factorModel
if (as.logical(fixALatent))
testModel <- mxModel(testModel, mxPath(from="one", to=latents, arrows=1, values=0, labels=c("MG", "MH"), free=c(TRUE, FALSE)) )
# Test unsaturated latent covariance matrix
imxPPML.Test.Battery(testModel, verbose=TRUE, testPermutations=FALSE, tolerances=c(NA, .0001, .0001) ) # NA -> Don't check covariance data w/ means
# Test saturated latent covariance matrix
testModel <- mxModel(testModel, satPath)
imxPPML.Test.Battery(testModel, verbose=TRUE, testPermutations=FALSE, tolerances=c(NA, .0001, .0001) ) # NA -> Don't check covariance data w/ means
}
|
