confidence intervals with mxTryHardOrdinal optimizer
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lior abramson
Joined: 07/21/2017
Hi,
I would like to compute confidence intervals for the ACE components in a univariate twins analysis.
Since I have categorical covariates in the model, the only way I could make the model work was by replacing the mxRun optimizer with mxTryHardOrdinal optimizer. However, according to the R documentation of the mxCI function, the mxCI could not run with other optimizers except for mxRun. Similarly, even though I ask for "intervals=TRUE" in the model, the lbound and ubound of the summary output are NAs.
I would like to compute confidence intervals for the ACE components in a univariate twins analysis.
Since I have categorical covariates in the model, the only way I could make the model work was by replacing the mxRun optimizer with mxTryHardOrdinal optimizer. However, according to the R documentation of the mxCI function, the mxCI could not run with other optimizers except for mxRun. Similarly, even though I ask for "intervals=TRUE" in the model, the lbound and ubound of the summary output are NAs.
I would like to ask:
1) is this really the problem?
2) if it is, how would you advice me to calculate the confidence intervals?
Thank you so much,
Lior
You should probably post the
mxTryHardOrdinal(). You need to include the appropriate MxInterval object (which is created frommxCI()) in yourmxModel()statement, and include argumentintervals=TRUEtomxTryHardOrdinal().Log in or register to post comments
In reply to You should probably post the by AdminRobK
confidence intervals
In the meantime I decided to drop one of my covariates and now I can run the model with mxRun. This is the syntax, I hope it would help to understand the problem.
# Select Variables for Analysis
selVars <- c('NAFAC_1_Res','NAFAC_2_Res','LabHome6')
aceVars <- c("A1","C1","E1","A2","C2","E2")
# Select Data for Analysis
mzM <- subset(D.impWide, zygosity==1 & sex_a=='sex1',selVars)
mzF <- subset(D.impWide, zygosity==1 & sex_a=='sex2',selVars)
dzM <- subset(D.impWide, zygosity==2 & sex_a=='sex1',selVars)
dzF <- subset(D.impWide, zygosity==2 & sex_a=='sex2',selVars)
# Path objects for Multiple Groups
manifestVars=selVars
latentVars=aceVars
# specify paths for the variances and means of the latent variables
latVariances <- mxPath( from=aceVars, arrows=2, free=FALSE, values=1 )
varCovar <- mxPath( from= c('LabHome6'), arrows=2, free=(c(FALSE)), values=1, label='placeVar' )
# means of latent variables
latMeans <- mxPath( from="one", to=aceVars, arrows=1, free=FALSE, values=0 )
#specify paths for the means of the observed variables. Because the scores are residual scores from sex and age, I constrain the means to be the same for boys and girls.
obsMeansM <- mxPath( from="one", to=selVars, arrows=1, free=c(T,T,F), values=c(StartMean, StartMean, 0), labels=c("meanFAC","meanFAC","meanPlace") )
obsMeansF <- mxPath( from="one", to=selVars, arrows=1, free=c(T,T,F), values=c(StartMean, StartMean, 0), labels=c("meanFAC","meanFAC","meanPlace") )
# path coefficients for twin 1.
pathAceTM1 <- mxPath( from=c("A1","C1","E1", "LabHome6"), to="NAFAC_1_Res", arrows=1, free=TRUE, values=StartVar.ACE,label=c("aM","cM","eM","place") )
pathAceTF1 <- mxPath( from=c("A1","C1","E1","LabHome6"), to="NAFAC_1_Res", arrows=1, free=TRUE, values=StartVar.ACE, label=c("aF","cF","eF","place") )
# path coefficients for twin 2
pathAceTM2 <- mxPath( from=c("A2","C2","E2","LabHome6"), to="NAFAC_2_Res", arrows=1,free=TRUE, values=StartVar.ACE, label=c("aM","cM","eM", "place") )
pathAceTF2 <- mxPath( from=c("A2","C2","E2", "LabHome6"), to="NAFAC_2_Res", arrows=1,free=TRUE, values=StartVar.ACE, label=c("aF","cF","eF", "place") )
# covariance between C1 & C2
covC1C2 <- mxPath( from="C1", to="C2", arrows=2,free=FALSE, values=1 )
# covariance between A1 & A2 in MZ's
covA1A2_MZ <- mxPath( from="A1", to="A2", arrows=2, free=FALSE, values=1 )
# covariance between A1 & A2 in DZ's
covA1A2_DZ <- mxPath( from="A1", to="A2", arrows=2, free=FALSE, values=.5 )
#call the data.frame with the MZ raw data, mzData, and the DZ raw data
dataMZM <- mxData( observed=mzM, type="raw" )
dataMZF <- mxData( observed=mzF, type="raw" )
dataDZM <- mxData( observed=dzM, type="raw" )
dataDZF <- mxData( observed=dzF, type="raw" )
# Combine Groups
#arrange together all the paths that are the same for MZ and DZ
pathsM <- list(latVariances, latMeans, obsMeansM, pathAceTM1, pathAceTM2, covC1C2, varCovar )
pathsF <- list( latVariances, latMeans, obsMeansF, pathAceTF1, pathAceTF2, covC1C2, varCovar )
#Build a model seperately for MZ and DZ and for each sex
modelMZM <- mxModel(model="MZM", type="RAM", manifestVars=selVars,
latentVars=aceVars, pathsM, threshold, covA1A2_MZ, dataMZM )
modelDZM <- mxModel(model="DZM", type="RAM", manifestVars=selVars,
latentVars=aceVars, pathsM, threshold, covA1A2_DZ, dataDZM )
modelMZF <- mxModel(model="MZF", type="RAM", manifestVars=selVars,
latentVars=aceVars, pathsF, threshold, covA1A2_MZ, dataMZF )
modelDZF <- mxModel(model="DZF", type="RAM", manifestVars=selVars,
latentVars=aceVars, pathsF, threshold, covA1A2_DZ, dataDZF )
minus2ll <- mxAlgebra(expression=MZM.fitfunction + DZM.fitfunction +MZF.fitfunction + DZF.fitfunction,
name="minus2loglikelihood" )
obj <- mxFitFunctionAlgebra( "minus2loglikelihood" )
modelACE.Hetero <- mxModel(model="ACE_Hetero", modelMZM, modelDZM,modelMZF, modelDZF, minus2ll, obj )
fitACE.Hetero <- mxRun(modelACE.Hetero, intervals=TRUE)
sumACE.Hetero <- summary(fitACE.Hetero)
mxCI('fitACE.Hetero')
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If you like it put it in your model
?mxCI. From thatBasically, you should create a confidence interval object with the mxCI function. Your call to this function should refer to free parameters, matrices, or algebras by name. You then put that CI object in the model before you run it.
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In reply to If you like it put it in your model by mhunter
Thank you
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