95% CI of A,C,E estimates in Twin model

Posted on Thu, 12/02/2010 - 18:42

Sabha Joined: 05/18/2010

Forums

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.

Thanks in advance.

Replied on Fri, 12/03/2010 - 14:00

neale Joined: 07/31/2009

See this part of the wiki:

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

Replied on Tue, 12/07/2010 - 20:10

Sabha Joined: 05/18/2010

Thanks for your help. I was

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.

Replied on Thu, 12/09/2010 - 09:50

neale Joined: 07/31/2009

Same way, just use the names

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.

Replied on Fri, 07/29/2016 - 15:50

olleee Joined: 07/28/2016

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

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

Replied on Fri, 07/29/2016 - 16:19

AdminNeale Joined: 03/01/2013

Error message has been improved

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
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")
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.
library(OpenMx) # 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 )
aceMat <- mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=.5,labels=c("a","c","e"),name="ace")

StdVarCompAlg <- 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 )

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, aceMat, StdVarCompAlg, mxCI("StdVarComp"))

# Run Model summary(fitACE <- mxRun(modelACE, intervals=TRUE))

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

Replied on Tue, 10/03/2017 - 22:32

lior abramson Joined: 07/21/2017

ran into the same problem

Hi,
I know this is an old thread, but I ran into the same problem and would like to ask for your help.
I have been trying to adjust this code to mine, in order to get the CI of the ACE standardized variance components (a2/V etc..).

This is what I added:
#Confidence interval for the variance components aceMat <-mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=StartVar.ACE,labels=c("a","c","e"),name="ace") mxal <-mxAlgebra( (ace%^%2) %x% solve(t(ace)%*%ace), name="StdVarComp",dimnames=list(c("a2","c2","e2"),NULL) ) mxCI("StdVarComp")


obj <- mxFitFunctionMultigroup(c("MZM","DZM","MZF","DZF"))

modelACE.Homog <- mxModel(model="ACE_Homog",modelMZM, modelDZM,modelMZF, modelDZF, obj, aceMat,mxal, mxCI("StdVarComp") )

mxAutoStart(modelACE.Homog) fitACE.Homog <- mxTryHardOrdinal(modelACE.Homog, intervals=TRUE) sumACE.Homog <- summary(fitACE.Homog)
And I got the following message:
Error: The reference 'StdVarComp' does not exist. It is used by named reference 'confidence interval StdVarComp'

I am guessing that I have a simple syntax mistake, but cannot find it.
Do you have any idea what I did wrong?

Thank you very much

Replied on Wed, 10/04/2017 - 11:10

AdminRobK Joined: 01/24/2014

I don't see anything wrong

I don't see anything wrong with your syntax. I'm curious what you get from



mxEval(StdVarComp, modelACE.Homog, TRUE)

and from



fitACE.Homog <- mxRun(modelACE.Homog, intervals=TRUE)

(i.e. using mxRun() instead of mxTryHardOrdinal() and without first using mxAutoStart()).

Replied on Thu, 12/09/2010 - 09:58

tbates Joined: 07/31/2009

To add to mikes reply: You

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.

Replied on Fri, 12/10/2010 - 02:53

Sabha Joined: 05/18/2010

You spelled my wish.

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.

Replied on Thu, 05/15/2014 - 01:35

danioreo Joined: 05/15/2014

Hi, I followed

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!

Replied on Thu, 05/15/2014 - 11:47

RobK Joined: 04/19/2011

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 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):
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"),
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.

Replied on Thu, 05/15/2014 - 22:52

danioreo Joined: 05/15/2014

Thank you for solving my problem

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.

Replied on Fri, 05/16/2014 - 18:29

RobK Joined: 04/19/2011

You're welcome. Glad to be

You're welcome. Glad to be of help.

Replied on Mon, 03/02/2015 - 13:13

martonandko Joined: 02/19/2015

95% CI using bootstrap

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?

Replied on Wed, 10/04/2017 - 10:10

AdminNeale Joined: 03/01/2013

mxBootstrap()

Yes, there's a recent implementation of bootstrapping in OpenMx, which avoids the model being interpreted repeatedly. See
?mxBootstrap
the function call looks like this:
mxBootstrap(model, replications=200, ...,
data=NULL, plan=NULL, verbose=0L,
parallel=TRUE, only=as.integer(NA),
OK=mxOption(model, "Status OK"), checkHess=FALSE)

I can't remember in which version this first appeared.

Replied on Mon, 04/30/2018 - 22:23

Nengzhi Joined: 04/30/2018

CI estimation of univariate genetic model

I‘m new to OpenMx and R. I analyzed my twin data via UnivariateTwinAnalysis_PathRaw.R. However, the result didn't contain the 95%CI for a2,c2,e2 and Δχ2,p value of different models. Could anyone please tell me how to change the scripts to obtain these results.
I have added these codes to the above scripts ,



ci        <- mxCI(c("StPathA","StPathC","StPathE","PropVA", "PropVC", "PropVE",

                     "corA","corC","corE", "corP"))

CholAceModel  <- mxModel( "CholACE", pars, modelMZ, modelDZ, minus2ll, obj, ci )

# Run Cholesky Decomposition ACE model CholAceFit <- mxRun(CholAceModel, intervals=T)
But i still can't obtain that result I wanted.

File attachments

Uni-forjiang.R

jiang.sav

Replied on Tue, 05/01/2018 - 13:59

AdminRobK Joined: 01/24/2014

Concerning the change in

Concerning the change in -2logL between two models, you can directly pull the -2logL value from a model's output slot, e.g.



LL_ACE <- twinACEFit$output$fit

I can tell from your script that you already know how to calculate LRT statistics, but to get a p-value from each, use pchisq(), with the appropriate df and with argument lower.tail=FALSE.

Concerning confidence intervals: the first argument to mxCI() has to be a vector of character strings, with each string referring to a named entity--a labeled path or parameter, an MxMatrix, or an MxAlgebra--in the MxModel object's namespace. The call to mxCI() in your post does not reference any named entities in the namespace of MxModel twinACEModel. As has been stated previously in this thread, you'll need to create MxAlgebras that calculate the quantities of interest, put them into your MxModel, and request CIs for them. For instance, to get a CI for the raw and standardized additive-genetic variance components, create the followoing objects,
va <- mxAlgebra(a^2, "Va") v <- mxAlgebra( (a^2)+(c^2)+(e^2), "Vp") stva <- mxAlgebra( Va/Vp, "StVa") ci <- mxCI(c("Va","StVa"))
, and put them into your MZ or DZ submodel (it should not matter which).

Replied on Tue, 05/01/2018 - 14:29

AdminNeale Joined: 03/01/2013

Yep, and mxCompare()

A simpler and more direct way to obtain a table of results including LRT deltas and p values is to use mxCompare(baseFittedModel, comparisonFittedModel).

Replied on Sun, 05/13/2018 - 11:03

Nengzhi Joined: 04/30/2018

P value

Thank you very much. You really gave me great help.The last problem for me about this scripts is to get a p-value from each model. You have told me to" use pchisq(), with the appropriate df and with argument lower.tail=FALSE." Because I really know little about R, could please give me an example in this scritps :



# 	AE model

# path coefficients for twin 1

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

                        free=c(T,F,T), values=c(.6,0,.6),  label=c("a","c","e") )

# path coefficients for twin 2

pathAceT2    <- mxPath( from=c("A2","C2","E2"), to=selVars[2], arrows=1,

                        free=c(T,F,T), values=c(.6,0,.6),  label=c("a","c","e") )
# Combine Groups

paths        <- list( latVariances, latMeans, obsMeans,

                        pathAceT1, pathAceT2, covC1C2 )

aceMat <- mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=.5,labels=c("a","c","e"),name="ace")

StdVarCompAlg <- 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 )

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" )

modelAE <- omxSetParameters(model=ACEFit,labels="c",free=FALSE,values=0,name="AE")

# Run Model

AEFit    <- mxRun(modelAE,intervals=TRUE)

AESum    <- summary(AEFit)

# Fit AE model

# -----------------------------------------------------------------------------
# Generate & Print Output

M  <- mxEval(mean, AEFit)

A  <- mxEval(a*a, AEFit)

C  <- mxEval(c*c, AEFit)

E  <- mxEval(e*e, AEFit)

V  <- (A+C+E)

a2 <- A/V

c2 <- C/V

e2 <- E/V

estAE <- rbind(cbind(A, C, E),cbind(a2, c2, e2))

LL_AE <- mxEval(fitfunction, AEFit)

LRT_ACE_AE <- LL_AE - LL_ACE
estACE

estAE

LRT_ACE_AE

# Get Model Output

# -----------------------------------------------------------------------------
# AE model details

AESum

Replied on Mon, 05/14/2018 - 10:01

AdminRobK Joined: 01/24/2014

ProTip: It's a good idea to

ProTip: It's a good idea to look at the help page for an R function that's unfamiliar to you before you use it. You can see the help page by entering the function symbol, preceded by a question mark, at the R prompt, e.g. ?pchisq.

Concerning your script in particular, try this:
pchisq(q=LRT_ACE_AE,df=1,lower.tail=FALSE)
The df=1 is because there is a difference of 1 in the number of free parameters in the ACE model versus the AE model. This command will give you the p-value for the test of the null hypothesis that C variance is zero (when both A variance and E variance are free).

Replied on Mon, 05/14/2018 - 13:03

AdminNeale Joined: 03/01/2013

mxCompare too

Rob's code works. Simpler to use, however, would likely be the mxCompare() function, to which you pass a model and a submodel and it works out the likelihood ratio test and p-value for you. Again ?mxCompare documentation is a good place to start.

Replied on Wed, 05/02/2018 - 22:19

Nengzhi Joined: 04/30/2018

CI estimation of univariate genetic model

I have change my scripts to this:



# Combine Groups

paths        <- list( latVariances, latMeans, obsMeans,

                      pathAceT1, pathAceT2, covC1C2 )
aceMat <- mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=.5,labels=c("a","c","e"),name="ace")

StdVarCompAlg <- 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 )

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, aceMat, StdVarCompAlg, mxCI("StdVarComp"))
# Run Model

ACEFit <- mxRun(modelACE,intervals=TRUE) ACESum <- summary(ACEFit) ACESum
Then, I obtained the CI for ACE. However, when I used the same scripts to run AE model as this:
# Combine Groups paths <- list( latVariances, latMeans, obsMeans, pathAceT1, pathAceT2, covC1C2 ) aceMat <- mxMatrix(type="Full",nrow=3,ncol=1,free=T,values=.5,labels=c("a","c","e"),name="ace") StdVarCompAlg <- 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 ) modelDZ <- mxModel(model="DZ", type="RAM", manifestVars=selVars, latentVars=aceVars, paths, covA1A2_DZ, dataDZ ) modelAE <- mxModel(model="AE", modelMZ, modelDZ, minus2ll, obj, aceMat, StdVarCompAlg, mxCI("StdVarComp"))
an error happened:
> # Run Model > AEFit <- mxRun(modelAE,intervals=TRUE) Error: In model 'AE' the name 'c' is used as a free parameter in 'AE.ace' and as a fixed parameter in 'MZ.A' and 'DZ.A'
I don't know how to solve the question, could anyone can help me?

Replied on Thu, 05/03/2018 - 10:19

AdminRobK Joined: 01/24/2014

omxSetParameters()

There's no need to reconstruct a new MxModel object from its constituent parts just to fix the shared-environmental variance to zero. Just do



modelAE <- omxSetParameters(model=ACEfit,labels="c",free=FALSE,values=0,name="AE")

and proceed.

Replied on Tue, 05/08/2018 - 08:54

Nengzhi Joined: 04/30/2018

modelE

Thank you very much. I have obtained the result of ACE,AC&CE. However, when I change:
modelE <- omxSetParameters(model=ACEFit,labels="a",labels="c",free=FALSE,values=0,name="E")
There was an error:
Error in omxSetParameters(model = ACEFit, labels = "a", labels = "c", :
formal argument "labels" matched by multiple actual arguments
Could u please help me rewrite the scripts to get the result of E model?

Replied on Tue, 05/08/2018 - 10:15

AdminRobK Joined: 01/24/2014

c() is your friend

This,



modelE <- omxSetParameters(model=ACEFit,labels="a",labels="c",free=FALSE,values=0,name="E")

isn't valid R syntax. You can't pass two different values for one function argument specified by name like that. What you want to do instead is



modelE <- omxSetParameters(model=ACEFit,labels=c("a","c"),free=FALSE,values=0,name="E")

. The function c() is for concatenating multiple values into a vector.

It would also work to fix the two free parameters via two calls to omxSetParameters():
modelE <- omxSetParameters(model=ACEFit,labels="a",free=FALSE,values=0,name="E") modelE <- omxSetParameters(model=modelE,labels="c",free=FALSE,values=0)
That's a bit inelegant, though.

Replied on Mon, 05/14/2018 - 20:55

Nengzhi Joined: 04/30/2018

Thank you for your help

It's very lucky for me to find this forum. Rob and Neale are so nice!
Best wishes to you!

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95% CI of A,C,E estimates in Twin model

See this part of the wiki:

Thanks for your help. I was

Same way, just use the names

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

Error message has been improved

ran into the same problem

I don't see anything wrong

To add to mikes reply: You

You spelled my wish.

Hi, I followed

Add algebras to your mxModel and request CIs for them

Thank you for solving my problem

You're welcome. Glad to be

95% CI using bootstrap

mxBootstrap()

CI estimation of univariate genetic model

Concerning the change in

Yep, and mxCompare()

P value

ProTip: It's a good idea to

mxCompare too

CI estimation of univariate genetic model

omxSetParameters()

modelE

c() is your friend

Thank you for your help

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