#######################################################
#-----------------------------------------------------
# fit and compare models
#-----------------------------------------------------
#######################################################

# fit Saturated model

fitSATURATEDmodel <- mxRun(SATURATEDmodel)

summary(fitSATURATEDmodel)

mxEval(Saturated.cormz,fitSATURATEDmodel)
mxEval(Saturated.cordz,fitSATURATEDmodel)


#----------------------------------------------------
# FIT 0 GEcovSIMPLEX with all parameters in place
#----------------------------------------------------

fitGEcovSIMPLEX <- mxRun(GEcovSIMPLEXmodel)

summary(fitGEcovSIMPLEX)

fit0=cbind(round(fitGEcovSIMPLEX@output$estimate,3),round(fitGEcovSIMPLEX@output$standardErrors,3))#  

mxCompare(fitSATURATEDmodel,fitGEcovSIMPLEX)  

#  conclusion:  ?


# From here on forward, we are going to drop parameters to replicate the findings in our Flynn effect paper.
# We will give you the label names of the parameters to be dropped. 

# TASK: 
# [1] adapt the code such that the parameters are dropped using the function: omxSetParameters


#----------------------------------------------------
# FIT 1 GEcovSIMPLEX with dropping the time specific a
#----------------------------------------------------
# drop parameter of time specific additive genetic variance
# labels is 'ar'

GEcovSIMPLEXmodel_1 = omxSetParameters(GEcovSIMPLEXmodel, labels=c('ar'), free = c(FALSE), values = c(0))
fitGEcovSIMPLEX_1 <- mxRun(GEcovSIMPLEXmodel_1)

summary(fitGEcovSIMPLEX_1)

mxCompare(fitGEcovSIMPLEX,fitGEcovSIMPLEX_1)

#  conclusion:  ?

#----------------------------------------------------
# FIT 2 GEcovSIMPLEX 
#----------------------------------------------------
# drop transmission in E
# labels are 'be1','be2','be3'

GEcovSIMPLEXmodel_2 = omxSetParameters(GEcovSIMPLEXmodel_1, labels=c('be1','be2','be3'), free = c(FALSE), values = c(0))
fitGEcovSIMPLEX_2 <- mxRun(GEcovSIMPLEXmodel_2)

summary(fitGEcovSIMPLEX_2)

mxCompare(fitGEcovSIMPLEX_1,fitGEcovSIMPLEX_2)

#  conclusion:  ?

#----------------------------------------------------
# FIT 3 GEcovSIMPLEX
#----------------------------------------------------
# drop innovation of additive genetic variance in A
# labels are 'va3','va4'

GEcovSIMPLEXmodel_3 = omxSetParameters(GEcovSIMPLEXmodel_2, labels=c('va3','va4'), free = c(FALSE), values = c(0))
fitGEcovSIMPLEX_3 <- mxRun(GEcovSIMPLEXmodel_3)

summary(fitGEcovSIMPLEX_3)
mxCompare(fitGEcovSIMPLEX_2,fitGEcovSIMPLEX_3)

#  conclusion:  ?

#----------------------------------------------------
# FIT 4 GEcovSIMPLEX 
#----------------------------------------------------
# drop covariance in E
# labels are 'cee2','cee3','cee4'


GEcovSIMPLEXmodel_4 = omxSetParameters(GEcovSIMPLEXmodel_3, labels=c('cee2','cee3','cee4'), free = c(FALSE), values = c(0))
fitGEcovSIMPLEX_4 <- mxRun(GEcovSIMPLEXmodel_4)


summary(fitGEcovSIMPLEX_4)
mxCompare(fitGEcovSIMPLEX_3,fitGEcovSIMPLEX_4) 

#  conclusion:  ?

#----------------------------------------------------
# FIT 5 GEcovSIMPLEX 
#----------------------------------------------------
# set transmission in A equal
# labels are 'ba1','ba2','ba3'


GEcovSIMPLEXmodel_5 = omxSetParameters(GEcovSIMPLEXmodel_4, 
                     labels=c('ba1','ba2','ba3'), newlabels=c('ba1','ba1','ba1'), free = c(T), values = c(.8))

fitGEcovSIMPLEX_5 <- mxRun(GEcovSIMPLEXmodel_5)


summary(fitGEcovSIMPLEX_5)

mxCompare(fitGEcovSIMPLEX_4,fitGEcovSIMPLEX_5)

#  conclusion:  ?

#----------------------------------------------------
# FIT 6 GEcovSIMPLEX 
#----------------------------------------------------
# test if ph->e can be dropped
# labels are 'ph_e1w','ph_e1b'

GEcovSIMPLEXmodel_6 = omxSetParameters(GEcovSIMPLEXmodel_5, 
                     labels=c('ph_e1w','ph_e1b'), free = c(F), values = c(.0))

fitGEcovSIMPLEX_6 <- mxRun(GEcovSIMPLEXmodel_6)
mxCompare(fitGEcovSIMPLEX_5,fitGEcovSIMPLEX_6)

#  conclusion:  ?

#----------------------------------------------------
# Final comparison
#----------------------------------------------------
# Return to the last fitting model
# does the final model fit
# 

mxCompare(fitSATURATEDmodel,fitGEcovSIMPLEX_5)