In my twin model i calculate Rmz and Rdz from the saturated model, however when i run it (i run it on several variables) for a few variables CSOLNP fails to calculate confidence intervals, while SLSQP does caculate all. Is there a particular reason for this? Should i be concerned by this, or just use slsqp for the cases csolnp fails?

Here is the sat part, if it helps:

#sat model, rmz, rdz: PathMZ <- mxMatrix("Lower",nrow = 2,ncol = 2,free = TRUE,values=starting_value_pathcoeff,name = "PathMZ")#cholesky for pos definitness PathDZ <- mxMatrix("Lower",nrow = 2,ncol = 2,free = TRUE,values=starting_value_pathcoeff,name = "PathDZ")#cholesky for pos definitness expCovMZ <- mxAlgebra(expression = PathMZ%*%t(PathMZ),name = "expCovMZ") expCovDZ <- mxAlgebra(expression = PathDZ%*%t(PathDZ),name = "expCovDZ") Rmz <- mxAlgebra(expression = sqrt(vec2diag(1/diag2vec(expCovMZ)))%*%expCovMZ%*%sqrt(vec2diag(1/diag2vec(expCovMZ))), name = "Rmz") Rdz <- mxAlgebra(expression = sqrt(vec2diag(1/diag2vec(expCovDZ)))%*%expCovDZ%*%sqrt(vec2diag(1/diag2vec(expCovDZ))), name = "Rdz") expMZ <- mxExpectationNormal(covariance = "expCovMZ",means = "exp_mean_mz",dimnames = selectVariables) expDZ <- mxExpectationNormal(covariance = "expCovDZ",means = "exp_mean_dz",dimnames = selectVariables) MZmodel <- mxModel(intercepts,beta,dependent_vars_mz,PathMZ,Rmz, expCovMZ,exp_mean_mz,DataMZ,expMZ,fitML ,name="MZmodel") #MZmodel <- mxModel(MZmodel,mxCI(c("Rmz"))) DZmodel <- mxModel(intercepts,beta,dependent_vars_dz,PathDZ,Rdz, expCovDZ,exp_mean_dz,DataDZ,expDZ,fitML ,name="DZmodel") #DZmodel <- mxModel(DZmodel,mxCI(c("Rdz"))) fitmultiMLsat <- mxFitFunctionMultigroup(c("MZmodel.fitfunction","DZmodel.fitfunction")) SatModel <- mxModel("SAT",MZmodel,DZmodel,fitmultiMLsat) SatModel <- mxModel(SatModel,mxCI(c("MZmodel.Rmz","DZmodel.Rdz"))) Satfinal <- mxRun(SatModel, intervals = TRUE) sum_sat_raw <- summary(Satfinal)

It's probably nothing to worry about, but it's also a good idea to use

`summary(Satfinal,verbose=TRUE)`

to seewhythe confidence limits aren't being reported.Without going into a lot of detail, those two optimizers by default use a different internal representation of the confidence-limit search. There are also theoretical reasons why SLSQP would be expected to give better confidence intervals most of the time. If you want to use CSOLNP to fit your model, but use SLSQP to find confidence intervals, then run your MxModel with

`intervals=FALSE`

, and then use`omxRunCI()`

on your fitted MxModel (which uses SLSQP by default).Thank you!

I think then i will use SLSQP just for the variables where CSOLNP didn't work, because generally the intervals are a bit tighter when i use CSOLNP.

By the way, it's a diagnostic error: alpha level not reached infeasible non-linear constraint.