Make $ <- method for symmetric matrix enforce symmetry (other set cases too)
Currently, $<- on a Symm matrix allows asymetric assignment. That's bad.
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Make Iden subsetting return the data: mxMatrix("Iden", 2, 2)[1,2] # 0 not 1
Solution: convert to full prior to subsetting.
Matrices try to retain type, but may be best just to convert.
Watch with efficiency of storage vs. CPU
Good if this would be trapped at or before run time…
Matrices try to retain type, but may be best just to convert.
Watch with efficiency of storage vs. CPU
Good if this would be trapped at or before run time…
$ accessor support for mxModel manifestVars latentVars
the model$ accessor for manifestVars and for latentVars should return the contents of the eponymous model slots: model@manifestVars & model@latentVars
Add constraint check for mxCheckIdentification
The mxCheckIdentification() function should check to see if there are any MxConstraint objects. It should stop and give up in this case.
Confidence Interval Reference search broken
See this minimal working example.
library(OpenMx)
data(demoOneFactor)
# ===============================
# = Make and run a 1-factor CFA =
# ===============================
latents = c("G") # the latent factor
manifests = names(demoOneFactor) # manifest variables to be modeled
# ====================
# = Make the MxModel =
# ====================
m1 <- mxModel("One Factor", type = "RAM",
manifestVars = manifests, latentVars = latents,
mxPath(from = latents, to = manifests),
mxPath(from = manifests, arrows = 2),
- Read more about Confidence Interval Reference search broken
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Version 2.5.2 of OpenMx now available
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The newest release of OpenMx, version 2.5.2, is now available through CRAN and through our own repository.
Test loading saved R workspace images
We should have a test that loads a save workspace image, if we want to ensure that this behavior is backward compatible.
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Robustify mxRefModels
When the Cholesky of the pairwise complete sample covariance fails, try just starting with the variances and means.
http://openmx.psyc.virginia.edu/thread/4079#comment-6338
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implement mxModel(remove = TRUE, type = LISREL)?
Currently returns no-can-do.
If complications with LISREL, removing all exogenous can be solved would be handy and RAM-consistent
If complications with LISREL, removing all exogenous can be solved would be handy and RAM-consistent
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level 1 warning as first step toward more in-depth search
https://github.com/OpenMx/OpenMx/blob/master/R/MxModelFunctions.R#L127-L137
Add test script checking returning first row behavior?
What is proper behavior for this?
What is the value of submodel.likelihood + submodel. A[1,1] when submodel.A is “data.X”?
It depends on how many rows in are data. If nrow(data) == 1 then data.X is the data in the first row. If nrow(data) > 1 then data.X is set to NaN. (initial compute)
NaN is dangerous because OpenMx matrix algebras are NA/NaN unsafe.
Add test script checking returning first row behavior?
What is proper behavior for this?
What is the value of submodel.likelihood + submodel. A[1,1] when submodel.A is “data.X”?
It depends on how many rows in are data. If nrow(data) == 1 then data.X is the data in the first row. If nrow(data) > 1 then data.X is set to NaN. (initial compute)
NaN is dangerous because OpenMx matrix algebras are NA/NaN unsafe.