Hi OpenMx Team - thanks for a great R package!

After using OpenMx for some time now, I have a few suggestions that I'll put forth as things to think about. There may be reasons not to implement some or all of them, but in the spirit of trying to help I thought I would pass them along. I've included numbered suggestions below, but the numbers are arbitrary.

1.) I think fixed parameters should be included as part of the output. If someone has only the output, they will not necessarily know what paths were fixed and what those paths were fixed to. It seems it would

be easy to handle this be including as part of the output a row that has the name (where "labels" was used) in the mxPath call and the estimate would be the fixed value and the "Std.Error" as NA or something else to denote that it is not computed (because it is fixed).

2.) Confidence intervals for the RMSEA should be included. This can be done via the MBESS package as:

require(MBESS)

ci.rmsea(rmsea=.055, df=35, N=500, conf.level = 0.95)

But, the functions ci.rmsea uses can be pulled out and included in OpenMx so as not to rely on another package.

3.) I don't see much of a benefit of including the "elapsed time" and all of the calculation timers in the output. Perhaps if someone wants to request it, but it seems unnecessary in general.

4.) Having easy to obtain bootstrap confidence interval integration via the boot package would be great. For example, options such as ciBoot=TRUE (and then compute both BCa and percentile) and B=1000 (for the number of bootstrap replications) could be included and the rest of the process done behind the scenes.

5.) For the summary of the variables that is part of the output, I think the variance or standard deviation should also be included. I think OpenMx calls the R function summary(), which, for whatever reason does not have the variance or standard deviation included in its output (I've always wondered why it didn't include the variance or the standard deviation).

6.) Building some intelligence into the labeling of estimates so that so many labels do not have to be inserted manually would be great. Default values could be the variable name(s) with things like "coef" or "var" or "cor" in front to identify what they are, but the users could specify their own labels.

7.) Consider including the confidence intervals whenever intervals=TRUE. Having to specify a new mxCI object separately is awkward (especially since an option seems to exists via intervals=TRUE, even though that must be specified when mxCI is used). I know there was some discussion about how to implement confidence intervals on the Wiki among the team members, but it seems like there is an extra step one has to do here and thus simplification would be great.

8.) Including z-values as well as the corresponding p-values for the test of the null hypothesis (that the parameter equals zero) in the output for parameter estimates would be great (and is standard practice in other programs). Or, if not including them by default, a simple option to do this: pvalues=TRUE.

9.) I know some folks don't like fit indices, but others do. It would be nice if there was a way to easily obtain several of the popular fit indices. RMSEA is produced by default, but perhaps some helper function(s) could be used to easily obtain other fit indices.

10.) Perhaps this exists but if so I don't know about it and couldn't find anything via a search: consider including an easy way to obtain a standardized solution. For example, for a model fitted to raw data or a covariance matrix, having an option such as standarizedSolution=TRUE would help folks interpret their output. This is different than using a correlation matrix from the outset, as the distribution theory differers for raw vs. standardized data (due to the fact that the process of standardization is based on random variables, namely the variance of the variables). Ideally the appropriate standard errors could be given (RAMONA did this in Systat) for the standardized solution. But, even if only the point estimates were given, that would be helpful.

Thanks so much for your work on OpenMx - the community really appreciates it!

Ken