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presence of mxCI breaks Standard Error reporting

Submitted by neale on

Adding mxCI() to a model makes all SE's reported as NaN. I don't think it should.

data(demoOneFactor)

Rff <- mxMatrix(type="Stand",nrow=1,ncol=1,free=F,name="Rff")
L <- mxMatrix(type="Full",nrow=5,ncol=1,free=T,values=0.2,labels=paste("l",1:5,sep=""),name="L")
I <- mxMatrix(type="Iden",nrow=5,ncol=5,name="I")
C <- mxAlgebra(L %*% Rff %*% t(L),name="C")
U <- mxAlgebra(I-(I*C),name="U")
SD <- mxMatrix(type="Full",nrow=5,ncol=1,free=T,values=0.6,
labels=c("sd1","sd2","sd3","sd4","sd5"),lbound=0,name="SD")
Sigma <- mxAlgebra( (C+U) * (SD%*%t(SD)), name="Sigma", dimnames=list(colnames(demoOneFactor),colnames(demoOneFactor)))

factorModelStand <- mxModel(
"1FactorStd", Rff, L, I, C, U, SD, Sigma,
mxMLObjective(covariance="Sigma"),
# mxCI("L"),
mxData(observed=cov(demoOneFactor), type="cov", numObs=500)
)
run_fms <- mxRun(factorModelStand,intervals=T)
summary(run_fms)

factorModelStand <- mxModel(
"1FactorStd", Rff, L, I, C, U, SD, Sigma,
mxMLObjective(covariance="Sigma"),
mxCI("L"),
mxData(observed=cov(demoOneFactor), type="cov", numObs=500)
)
run_fms <- mxRun(factorModelStand,intervals=T)
summary(run_fms)

Submitted by tbates on Wed, 03/26/2014 - 17:07

This does seem wrong, and is still true with current beta

packageVersion("OpenMx")
[1] ‘999.0.0.3156’

data(demoOneFactor)
m1 <- mxModel("1FactorStd",
mxMatrix(type = "Stand", nrow = 1, ncol = 1, free = F, name = "Rff"),
mxMatrix(type = "Full", nrow = 5, ncol = 1, free = T, values = 0.2, labels = paste0("l", 1:5), name = "L"),
mxMatrix(type = "Iden", nrow = 5, ncol = 5, name = "I"),
mxAlgebra(L %*% Rff %*% t(L), name = "C"),
mxAlgebra(I-(I*C), name = "U"),
mxMatrix(type = "Full", nrow = 5, ncol = 1, free = T, values = 0.6, labels = paste0("sd",1:5), lbound = 0, name = "SD"),
mxAlgebra( (C + U) * (SD%*%t(SD)), name = "Sigma", dimnames = list(colnames(demoOneFactor), colnames(demoOneFactor))),
mxMLObjective("Sigma"),
mxData(cov(demoOneFactor), type="cov", numObs=500)
)
m1 <- mxRun(m1,intervals=T)
summary(m1)$parameters[,1:6]
# name matrix row col Estimate Std.Error
# 1 l1 L 1 1 0.8913096 0.009723307
# 2 l2 L 2 1 0.9325548 0.006408531
# 3 l3 L 3 1 0.9438468 0.005514373
# 4 l4 L 4 1 0.9623626 0.004007500
# 5 l5 L 5 1 0.9725557 0.003275464
# 6 sd1 SD 1 1 0.4455831 0.014104647
# 7 sd2 SD 2 1 0.5400880 0.017096124
# 8 sd3 SD 3 1 0.6115843 0.019359291
# 9 sd4 SD 4 1 0.7302594 0.023115874
# 10 sd5 SD 5 1 0.8187199 0.025916025

m2 <- mxRun(mxModel(m1, mxCI("L"), name= "withCI"), intervals = T)
a = summary(m2)
round(a$CI,2)
# lbound estimate ubound
# withCI.L[1,1] 0.87 0.89 0.91
# withCI.L[2,1] 0.92 0.93 0.94
# withCI.L[3,1] 0.93 0.94 0.95
# withCI.L[4,1] 0.95 0.96 0.97
# withCI.L[5,1] 0.97 0.97 0.98

a$parameters[,1:6]
# name matrix row col Estimate Std.Error
# 1 l1 L 1 1 0.8913096 NA
# 2 l2 L 2 1 0.9325548 NA
# 3 l3 L 3 1 0.9438468 NA
# 4 l4 L 4 1 0.9623626 NA
# 5 l5 L 5 1 0.9725557 NA
# 6 sd1 SD 1 1 0.4455831 NA
# 7 sd2 SD 2 1 0.5400880 NA
# 8 sd3 SD 3 1 0.6115843 NA
# 9 sd4 SD 4 1 0.7302594 NA
# 10 sd5 SD 5 1 0.8187199 NA

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Submitted by jpritikin on Wed, 03/26/2014 - 20:02

Verify that SVN 3209 fixes it and close the bug.

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Submitted by mhunter on Wed, 03/26/2014 - 20:40

This works.


require(OpenMx)
data(demoOneFactor)
m1 <- mxModel("1FactorStd",
mxMatrix(type = "Stand", nrow = 1, ncol = 1, free = F, name = "Rff"),
mxMatrix(type = "Full", nrow = 5, ncol = 1, free = T, values = 0.2, labels = paste("l", 1:5, sep=''), name = "L"),
mxMatrix(type = "Iden", nrow = 5, ncol = 5, name = "I"),
mxAlgebra(L %*% Rff %*% t(L), name = "C"),
mxAlgebra(I-(I*C), name = "U"),
mxMatrix(type = "Full", nrow = 5, ncol = 1, free = T, values = 0.6, labels = paste("sd",1:5, sep=''), lbound = 0, name = "SD"),
mxAlgebra( (C + U) * (SD%*%t(SD)), name = "Sigma", dimnames = list(colnames(demoOneFactor), colnames(demoOneFactor))),
mxMLObjective("Sigma"),
mxData(cov(demoOneFactor), type="cov", numObs=500)
)
#Warning message:
#In mxMLObjective("Sigma") :
# Objective functions like mxMLObjective() have been deprecated in favor of expectation and fit functions.
# Please use mxExpectationNormal(covariance= , ...) instead, and add a call to mxFitFunctionML()
# See examples at help(mxExpectationNormal)
m1 <- mxRun(m1,intervals=T)
#Running 1FactorStd
summary(m1)$parameters[,1:6]
# name matrix row col Estimate Std.Error
#1 l1 L 1 1 0.8913093 0.009723289
#2 l2 L 2 1 0.9325546 0.006408525
#3 l3 L 3 1 0.9438466 0.005514378
#4 l4 L 4 1 0.9623625 0.004007501
#5 l5 L 5 1 0.9725556 0.003275467
#6 sd1 SD 1 1 0.4455827 0.014104545
#7 sd2 SD 2 1 0.5400874 0.017095994
#8 sd3 SD 3 1 0.6115837 0.019359145
#9 sd4 SD 4 1 0.7302586 0.023115682
#10 sd5 SD 5 1 0.8187190 0.025915812
m2 <- mxRun(mxModel(m1, mxCI("L"), name= "withCI"), intervals = T)
#Running withCI
a = summary(m2)
round(a$CI,2)
# lbound estimate ubound
#withCI.L[1,1] 0.87 0.89 0.91
#withCI.L[2,1] 0.92 0.93 0.94
#withCI.L[3,1] 0.93 0.94 0.95
#withCI.L[4,1] 0.95 0.96 0.97
#withCI.L[5,1] 0.97 0.97 0.98
a$parameters[,1:6]
# name matrix row col Estimate Std.Error
#1 l1 L 1 1 0.8913093 0.009723313
#2 l2 L 2 1 0.9325546 0.006408544
#3 l3 L 3 1 0.9438466 0.005514383
#4 l4 L 4 1 0.9623625 0.004007503
#5 l5 L 5 1 0.9725556 0.003275467
#6 sd1 SD 1 1 0.4455827 0.014104607
#7 sd2 SD 2 1 0.5400874 0.017096070
#8 sd3 SD 3 1 0.6115837 0.019359224
#9 sd4 SD 4 1 0.7302586 0.023115792
#10 sd5 SD 5 1 0.8187190 0.025915938

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