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)