OpenMx version 2.7.9 is now available through CRAN and through our own package repository.
The character string "one" is now a reserved name in OpenMx, because RAM-type MxModels use a dummy variable of that name in their model for the mean vector. This means that using the name "one" for any object in an MxModel will now result in an error.
There is a new function, Hidden-Markov models--which include static mixture models as a special case--can now be fitted in OpenMx through the new Hidden-Markov expectation.
Two minor bugs in OpenMx now includes a flexible, options-rich implementation of the Nelder-Mead algorithm, a general-purpose derivative-free optimizer. It is intended for use when the gradient-descent optimizers have difficulty optimizing an MxModel involving ordinal data. Note that this optimizer's methods for handling MxConstraints are likely to be changed for the next version's release.
Rampart (OpenMx's multilevel SEM algorithm) now uses much less memory when working with large datasets.
The printed The documentation for With fitfunctions that can provide an analytic Hessian, it is now possible to provide NPSOL with an "internal" warm start by running a compute plan that places an MxComputeOnce step requesting the Hessian immediately before the MxComputeGradientDescent step that uses NPSOL.
Function Previously, if a free parameter's start value was zero, OpenMx would silently "nudge" the start value to 0.1. This behavior is now governed by mxOption "Nudge zero starts", the on-load default of which is "Yes".
Analyzing joint ordinal-and-continuous data with full-weighted WLS no longer triggers a warning message about bias. For details, see the documentation for If there are missing observations ( There is a new function, Advanced users are now able to provide analytic constraint Jacobians to Additional information about MxConstraints is now exported from the optimizer in the backed to the MxModel's 'output' slot. This feature is presently implemented for NPSOL and SLSQP. The additional diagnostics include the constraint function values and the corresponding Lagrange multipliers, the full constraint Jacobian, and the Hessian of the augmented Lagrangian at the solution.
A new function, New performance improvements have enabled RAM and LISREL MxModels to analyze continuous raw data almost as quickly as covariance-input data in many cases. The means matrix is also optimized more efficiently.
Evaluation of the GREML fitfunction's analytic derivatives is now much faster. Specifically, the computational effort involved is now load-balanced among multiple threads, and refactoring the code has eliminated unnecessary computational bottlenecks and has reduced memory demand.
Error reporting is much improved throughout OpenMx.
Function Function By default, Diagnostics for confidence intervals are now displayed in the output of When evaluating raw-data row loglikelihoods in a row-wise parallel manner, OpenMx now dynamically balances the computational workload among multiple threads using empirical times elapsed.
Continuous-time state-space models now allow non-invertible ("drift" or "dynamics") SLSQP now ignores inactive inequality constraints and extraneous equality constraints, and correctly reports when inequality constraints cannot be satisfied.
The Wu & Neale (2012) adjustment for parameters with upper or lower bounds has been implemented for confidence intervals, and is applied by default. Note that this is a backward-incompatible change in default behavior. To disable the adjustment, provide argument Function Function The following functions are now usable as part of MxAlgebras: The documentation for The documentation for Functions A bug involving
The most serious issue with the beta release of version 2.7 (v2.7.4) was its oversensitivity to start values. This issue has been resolved for v2.7.9.
Formerly, if an MxModel was using the default compute plan, then For use in MxAlgebras, functions Scores calculated by Several bugs in the sufficient-statistic optimization of Rampart (OpenMx's multilevel SEM algorithm) have been repaired.
Running an MxModel containing an MxAlgebra that uses Running a FIML analysis with a dataset containing a large (>1000) number of endogenous variables no longer ends in an error.
Previously, using NPSOL or SLSQP to run an MxModel containing an MxConstraint that involved fixed parameters may have resulted in an error. This bug has been repaired.
Using An unnecessary and slow matrix-by-matrix multiplication is no longer carried out when the optimizer invokes the GREML fitfunction to obtain only the fitfunction value (and not any analytic derivatives).
The conditions under which NPSOL expects the sign of an analytic constraint Jacobian to be positive versus negative are not simple. We expect that a future release of OpenMx will address this issue with clearer documentation and/or a streamlined backend interface with NPSOL.
When using SLSQP with an MxModel containing MxConstraints, OpenMx has no way of checking the optimality conditions of SLSQP's solution. Therefore, under such conditions, OpenMx will not return status code 5 or 6 when it actually should. This bug is expected to be repaired in a future release.
If an object of class 'MxRAMModel' is loaded from an .RData file before the OpenMx package is loaded into R's workspace, then the methods (e.g., On 32-bit Windows, relative to version 2.6, version 2.7 has more difficulty reaching the global fitfunction minimum with NPSOL in MxModels involving ordinal data.
The most significant change between OpenMx v2.6 and v2.7 is that CSOLNP is now the on-load default optimizer. Use mxOption() to switch optimizers, e.g. mxOption(NULL,"Default optimizer","SLSQP") to switch to SLSQP (the default since OpenMx v2.2) or mxOption(NULL,"Default optimizer","NPSOL") to switch to NPSOL (the only optimizer prior to v2.2 of OpenMx, and in classic Mx before it).
New features, performance improvements, and bug-fixes in OpenMx v2.7.9 include:
mxAutoStart(), which automatically picks very good starting values for a variety of models.
mxRefModels() have been repaired.
summary() output for MxModel objects is now easier to read and more informative.
mxOption() now correctly explains how OpenMx's interface with NPSOL uses the "Feasibility tolerance" option.
mxEval() now works with expressions that reference elements of MxAlgebras and MxMatrices via square brackets.
mxFitFunctionWLS() now includes an internal standardization that allows the scale parameters for ordinal variables to be estimated. This allows any ML ordinal model to be estimated as a WLS model (unless it uses definition variables) by merely switching (1) the mxFitFunctionML() with mxFitFunctionWLS(), and (2) the mxData(..., type='raw') with mxDataWLS(...)..
mxDataWLS().
NAs) in the dataset, mxFactorScores() now requires the user to provide a value for its new argument, minManifests, which sets a minimum count of non-NA scores to be present for each data row, below which the function will not calculate factor-score estimates for that row. Note that this is a backwards-incompatible change in behavior.
mxSE(), which calculates delta-method standard errors for arbitrary expressions, named entities, and algebras. Note that it requires that standard errors (really, a valid Hessian matrix) be available for the MxModel's free parameters.
mxRun() now prints non-persistent feedback messages about optimization progress.
mxConstraint() (but see the related "known issue" below). This feature is presently supported for NPSOL and SLSQP. Providing analytic Jacobians can significantly reduce the number of fitfunction evaluations that the optimizer needs to reach a solution.
omxDefaultComputePlan(), which creates a "default" MxComputeSequence, has been implemented. This new function is intended for cases where the user wants to run an MxModel with the same compute plan that would ordinarily be generated automatically at runtime, except for a few changes.
mxGenerateData() can now accept a dataframe as input instead of an MxModel, in which case it will return data based on a saturated multivariate-normal model.
mxGenerateData() is now compatible with joint ordinal-and-continuous datasets.
mxGenerateData() now approximates the missingness pattern of the original data. Note that this is a backwards-incompatible change in default behavior. The previous default behavior can be restored with argument use.miss=FALSE.
summary() with argument verbose=TRUE.
A matrices.
boundAdj=FALSE to mxCI().
mxRefModels() now correctly handles MxModels with a single-variable dataset.
mxTryHard() and its ilk now no longer compute the numerical Hessian and standard errors when there are MxConstraints in the model. This makes its behavior both consistent with mxRun(), and correct in the general case, as OpenMx's numerical standard errors will not in general be valid in the presence of MxConstraints.
dchisq(), pchisq(), dbinom(), pbinom(), dcauchy(), pcauchy().
mxOption() and mxComputeGradientDescent() has been expanded and clarified.
mxFitFunctionR() now warns against defining the fitfunction with functions that call OpenMx's backend (most notably omxMnor()), because doing so can crash R.
mxRun() and mxTryHard() et al. previously ignored the values of certain mxOptions if those options were set specifically to the MxModel being run, and instead used those options' globally set values. This bug has been repaired.
omxSetParameters() and global R option mxCondenseMatrixSlots set to TRUE has been repaired. This bug would have most likely manifested itself as mysterious errors at runtime involving the internal function 'setParametersMatrix'.A number of known issues with the beta release of version 2.7 (which was v2.7.4) have been resolved for v2.7.9:
mxRun() would silently override the values of mxOptions "Gradient step size", "Gradient iterations", and 'Function precision". The on-load default for these options is now the character string "Auto", which is automatically overridden with sensible numerical values at runtime, as in previous versions. However, if the user sets his/her own numerical values for these options prior to runtime, then mxRun() will respect those user-supplied values.
dnbinom() and pnbinom() have been replaced with omxDnbinom() and omxPnbinom(); these new functions are compatible with mxEval().
mxKalmanScores() are now correct.
omxAllInt() no longer ends with an error if omxAllInt() returns a probability of zero.
mxGetExpected() on an MxModel containing submodels that refer to the container no longer results in an error.
Some known issues with v2.7.9 exist:
summary()) defined for that class will not work.