Can anyone help me with the function or syntex needed to calculate the 95% CI for A,C,E estimates in the existing
UnivariateTwinAnalysis_MatrixRaw.R script.

Add algebras to your mxModel and request CIs for them

Do you want confidence intervals for the standardized path coefficients connecting the latent A, C, and E to the manifest variables? Or do you want confidence intervals for the standardized biometric variance components?

If you want them for the variance components, try adding something like the following to either your MZ-twin or DZ-twin submodel (it should not matter which):

I am assuming here that the single-headed paths going from the latent A, C, and E to the manifest variables are respectively labeled "a", "c", and "e", as in UnivariateTwinAnalysis_PathRaw.R . Also, depending on where in the mxModel() statement you put this code, you might need to delete that last comma. Then, when you use mxRun(), be sure to include argument intervals=TRUE. You can see the CIs in the output from summary(twinACEFit) or whatever.

What the code is doing is creating an mxMatrix to hold the path coefficients, because we're going to calculate something from them with an mxAlgebra, and algebras require matrices. What the algebra does is square each path coefficient, turning them into raw (unstandardized) variance components, and then divide them by the sum of their squares which is equal to the total phenotypic variance. The result is that raw variance components get divided by total variance, yielding standardized components--estimates of the phenotype's narrow-sense heritability, shared-environmentality, and unshared-environmentality.

What I want are the confidence intervals for the standardized biometric variance components (a2, c2, e2). My problem got solved after adding your script to the original file. The instruction is very helpful and clear. Thank you so much for your help.

I'm would like to get bootstrapped CI for my standardized path coefficients connecting the latent A, C, and E to the manifest variables, and to my standardized biometric variance components in a ACE model, defined using matrixes (I modified Neale's script (http://openmx.psyc.virginia.edu/thread/554) to add a further covariant, I couldn't figure it out using path definitions). Conventional 95% intervals won't work because of empirical under-identification. Is there an easier way then to run the model then taking bootstrapped samples and defining the mean and min max of the bootstrap distribution of the standardized path coefficients and variance components?

Thanks for your help. I was able to get the CI for A,C,E. I also would like to get the CI for standardized components ( e.g ACE.A/V,ACE.C/ACE.V,ACE.E/ACE.V). How do I compute it.

I tried various approaches to obtaining confidence intervals for a2, c2, and e2, but I always receive the same error message: "Error in free[i, j] : subscript out of bounds"

Can you please check what might be wrong with how I adjusted the UnivariateTwinAnalysis_PathRaw.R code (see below)? Or might there be a bug in the software version I'm running? Thanks!

The message you got was not very helpful. In a more recent build, I got:

> fitACE <- mxRun(modelACE, intervals=TRUE)
Error: Unknown reference to 'StdVarComp' detected in a confidence interval specification in model 'ACE'
You should check spelling (case-sensitive), and also addressing the right model: to refer to an algebra
See help(mxCI) to see how to refer to an algebra in a submodel.
FYI, I got as far as: runHelper(model, frontendStart, intervals, silent, suppressWarnings, unsafe, checkpoint, useSocket, onlyFrontend, useOptimizer)

This clue was enough for me to figure out that some of the objects were missing from your model. Specifically, because the lines

were not within the parentheses of an mxModel() function call, it could not find the objects to which the mxCI() referred. I modified the code to put the results of these commands into suitably named objects, and included these objects in the subsequent mxModel() call.

Hi, I followed UnivariateTwinAnalysis_PathRaw.R instead of UnivariateTwinAnalysis_MatrixRaw.R.

I'm pretty new to OpenMx and R. Could anyone help me generate the 95%CI for standardized A, C, E? Thanks!

Do you want confidence intervals for the standardized

path coefficientsconnecting the latentA,C, andEto the manifest variables? Or do you want confidence intervals for the standardized biometricvariance components?If you want them for the path coefficients, see my post in this other thread: http://openmx.psyc.virginia.edu/thread/2835 .

If you want them for the variance components, try adding something like the following to either your MZ-twin or DZ-twin submodel (it should not matter which):

I am assuming here that the single-headed paths going from the latent

A,C, andEto the manifest variables are respectively labeled`"a"`

,`"c"`

, and`"e"`

, as in UnivariateTwinAnalysis_PathRaw.R . Also, depending on where in the`mxModel()`

statement you put this code, you might need to delete that last comma. Then, when you use`mxRun()`

, be sure to include argument`intervals=TRUE`

. You can see the CIs in the output from`summary(twinACEFit)`

or whatever.What the code is doing is creating an

`mxMatrix`

to hold the path coefficients, because we're going to calculate something from them with an`mxAlgebra`

, and algebras require matrices. What the algebra does is square each path coefficient, turning them into raw (unstandardized) variance components, and then divide them by the sum of their squares which is equal to the total phenotypic variance. The result is that raw variance components get divided by total variance, yielding standardized components--estimates of the phenotype's narrow-sense heritability, shared-environmentality, and unshared-environmentality.What I want are the confidence intervals for the standardized biometric variance components (a2, c2, e2). My problem got solved after adding your script to the original file. The instruction is very helpful and clear. Thank you so much for your help.

You're welcome. Glad to be of help.

I'm would like to get bootstrapped CI for my standardized path coefficients connecting the latent A, C, and E to the manifest variables, and to my standardized biometric variance components in a ACE model, defined using matrixes (I modified Neale's script (http://openmx.psyc.virginia.edu/thread/554) to add a further covariant, I couldn't figure it out using path definitions). Conventional 95% intervals won't work because of empirical under-identification. Is there an easier way then to run the model then taking bootstrapped samples and defining the mean and min max of the bootstrap distribution of the standardized path coefficients and variance components?

See this part of the wiki: http://openmx.psyc.virginia.edu/wiki/mxCI-help

Thanks for your help. I was able to get the CI for A,C,E. I also would like to get the CI for standardized components ( e.g ACE.A/V,ACE.C/ACE.V,ACE.E/ACE.V). How do I compute it.

To add to mikes reply: You will need to create these standardizing algebras in your model, if they are not already present.

i.e., you can't just ask for mxCI("ACE.A/ACE.Vtot"), you have to create

mxAlgebra(ACE.A/ACE.Vtot, name="stdA")

mxCI(c('stdA')

On that note, wouldn't it be great if you could just include things in the mxCI statement and they would be automagically created!!

All the information is there.

You spelled my wish. Initially, I did try that way but it didn't work. Thanks a lot to both of you. It worked. Great help.

Same way, just use the names of the algebras instead of the names of the matrices in the list of elements for which you would like the CIs.

I tried various approaches to obtaining confidence intervals for a2, c2, and e2, but I always receive the same error message: "Error in free[i, j] : subscript out of bounds"

Can you please check what might be wrong with how I adjusted the UnivariateTwinAnalysis_PathRaw.R code (see below)? Or might there be a bug in the software version I'm running? Thanks!

---

# Load Data

data(twinData)

# Select Variables for Analysis

selVars <- c('bmi1','bmi2')

aceVars <- c("A1","C1","E1","A2","C2","E2")

# Select Data for Analysis

mzData <- subset(twinData, zyg==1, selVars)

dzData <- subset(twinData, zyg==3, selVars)

# Generate Descriptive Statistics

colMeans(mzData,na.rm=TRUE)

colMeans(dzData,na.rm=TRUE)

cov(mzData,use="complete")

cov(dzData,use="complete")

require(OpenMx)

# Path objects for Multiple Groups

manifestVars=selVars

latentVars=aceVars

# variances of latent variables

latVariances <- mxPath( from=aceVars, arrows=2,

+ free=FALSE, values=1 )

# means of latent variables

latMeans <- mxPath( from="one", to=aceVars, arrows=1,

+ free=FALSE, values=0 )

# means of observed variables

obsMeans <- mxPath( from="one", to=selVars, arrows=1,

+ free=TRUE, values=20, labels="mean" )

# path coefficients for twin 1

pathAceT1 <- mxPath( from=c("A1","C1","E1"), to="bmi1", arrows=1,

+ free=TRUE, values=.5, label=c("a","c","e") )

# path coefficients for twin 2

pathAceT2 <- mxPath( from=c("A2","C2","E2"), to="bmi2", arrows=1,

+ free=TRUE, values=.5, label=c("a","c","e") )

# covariance between C1 & C2

covC1C2 <- mxPath( from="C1", to="C2", arrows=2,

+ free=FALSE, values=1 )

# covariance between A1 & A2 in MZ twins

covA1A2_MZ <- mxPath( from="A1", to="A2", arrows=2,

+ free=FALSE, values=1 )

# covariance between A1 & A2 in DZ twins

covA1A2_DZ <- mxPath( from="A1", to="A2", arrows=2,

+ free=FALSE, values=.5 )

# Data objects for Multiple Groups

dataMZ <- mxData( observed=mzData, type="raw" )

dataDZ <- mxData( observed=dzData, type="raw" )

# Combine Groups

paths <- list( latVariances, latMeans, obsMeans,

+ pathAceT1, pathAceT2, covC1C2 )

mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=.6,labels=c("a","c","e"),name="ace")

mxAlgebra( (ace%^%2) %x% solve(t(ace)%*%ace), name="StdVarComp",dimnames=list(c("a2","c2","e2"),NULL) )

mxCI("StdVarComp")

modelMZ <- mxModel(model="MZ", type="RAM", manifestVars=selVars,

+ latentVars=aceVars, paths, covA1A2_MZ, dataMZ, mxCI("StdVarComp"))

modelDZ <- mxModel(model="DZ", type="RAM", manifestVars=selVars,

+ latentVars=aceVars, paths, covA1A2_DZ, dataDZ )

minus2ll <- mxAlgebra( expression=MZ.fitfunction + DZ.fitfunction,

+ name="minus2loglikelihood" )

obj <- mxFitFunctionAlgebra( "minus2loglikelihood" )

modelACE <- mxModel(model="ACE", modelMZ, modelDZ, minus2ll, obj )

# Run Model

fitACE <- mxRun(modelACE, intervals=TRUE)

Error in free[i, j] : subscript out of bounds

The message you got was not very helpful. In a more recent build, I got:

> fitACE <- mxRun(modelACE, intervals=TRUE)

Error: Unknown reference to 'StdVarComp' detected in a confidence interval specification in model 'ACE'

You should check spelling (case-sensitive), and also addressing the right model: to refer to an algebra

See help(mxCI) to see how to refer to an algebra in a submodel.

FYI, I got as far as: runHelper(model, frontendStart, intervals, silent, suppressWarnings, unsafe, checkpoint, useSocket, onlyFrontend, useOptimizer)

This clue was enough for me to figure out that some of the objects were missing from your model. Specifically, because the lines

were not within the parentheses of an mxModel() function call, it could not find the objects to which the mxCI() referred. I modified the code to put the results of these commands into suitably named objects, and included these objects in the subsequent mxModel() call.

The lower CI on the estimate of C which is already at zero sort of flunks out but could be considered to be zero.