fitFunction <- mxFitFunctionML(rowDiagnostics=TRUE)
mxOption(NULL,"Default optimizer","SLSQP") ### to avoid msg error: https://openmx.ssri.psu.edu/node/4238

quadratic_gm_lifesatisf <- mxModel('Quadratic Growth Model, Path Specification', fitFunction,
                                  type='RAM', mxData(observed=dfwide_OpenMx, type='raw'),
                                  manifestVars=c('life_satisf_2','life_satisf_3','life_satisf_4','life_satisf_5','life_satisf_6','life_satisf_7','life_satisf_8','life_satisf_9'),
                                  latentVars=c('eta_1','eta_2','eta_3'),
                                  # residual variances
                                  mxPath(from=c('life_satisf_2','life_satisf_3','life_satisf_4','life_satisf_5','life_satisf_6','life_satisf_7','life_satisf_8','life_satisf_9'),
                                         arrows=2, free=TRUE, values=1, labels='theta'),
                                  # latent variable variances & covariances
                                  mxPath(from=c('eta_1','eta_2','eta_3'), connect='unique.pairs',
                                         arrows=2, free=TRUE,
                                         values=c(1,1,1,0.5,0.5,0.5),
                                         labels=c('psi_11','psi_21','psi_31','psi_22','psi_32','psi_33')),
                                  # factor loadings
                                  mxPath(from='eta_1', to=c('life_satisf_2','life_satisf_3','life_satisf_4','life_satisf_5','life_satisf_6','life_satisf_7','life_satisf_8','life_satisf_9'),
                                         arrows=1, free=FALSE, values=1),
                                  mxPath(from='eta_2', to=c('life_satisf_2','life_satisf_3','life_satisf_4','life_satisf_5','life_satisf_6','life_satisf_7','life_satisf_8','life_satisf_9'),
                                         arrows=1, free=FALSE,
                                         values=c(0, 1, 2, 3, 4, 5, 6, 7)),
                                  mxPath(from='eta_3', to=c('life_satisf_2','life_satisf_3','life_satisf_4','life_satisf_5','life_satisf_6','life_satisf_7','life_satisf_8','life_satisf_9'),
                                         arrows=1, free=FALSE,
                                         values=c(0, 1 , 4, 9, 16, 25, 36, 49)),
                                  # latent variable means
                                  mxPath(from='one', to=c('eta_1','eta_2','eta_3'),
                                         arrows=1, free=TRUE, values=c(1,1,1),
                                         labels=c('alpha_1','alpha_2','alpha_3'))
) # Close Model

quadratic_gm_lifesatisf = mxModel(quadratic_gm_lifesatisf, mxCI(c('psi_11','psi_21','psi_31','psi_22','psi_32','psi_33', "theta", 'alpha_1','alpha_2','alpha_3'))) # list the things you want CIs for.
quadratic_gm_lifesatisf = mxRun(quadratic_gm_lifesatisf, intervals= T)