This document provides a quick overview of the capabilities of OpenMx in handling everything from simple calculations to optimization of complex multiple group models. Note that this overview focuses on matrix specification of OpenMx models, and that all these models can equally well be developed using path specification. We start with a matrix algebra example, followed by an optimization example. We end this quick overview with a twin analysis example, as the majority of prior Mx users will be familiar with this type of analysis. We plan to expand this overview with examples more relevant to other applications of structural equation modeling. While we provide the scripts here, we will not discuss every detail, as that is done more systematically in the next chapters with examples shown in two model styles and two data styles. We describe in detail a series of models using path specification in chapter two, followed by the same examples in matrix specification in chapter three.
The OpenMx scripts for the examples in this overview are available in the following files:
We will start by showing some of the main features of OpenMx using simple examples. For those familiar with Mx, it is basically a matrix interpreter combined with a numerical optimizer to allow fitting statistical models. Of course you do not need OpenMx to perform matrix algebra as that can already be done in R or by hand. However, to accommodate flexible statistical modeling of the type of models typically fit in Mx, Mplus or other SEM packages, special kinds of matrices and functions are required which are bundled in OpenMx. We will introduce key features of OpenMx using a matrix algebra example. Remember that R is object-oriented, such that the results of operations are objects, rather than just matrices, with various properties/characteristics attached to them. We will describe the script line by line; a link to the complete script is here.
Say, we want to create two matrices, A and B, each of them a ‘Full’ matrix with 3 rows and 1 column and with the values 1, 2 and 3, as follows:
we use the mxMatrix command, and define the type of the matrix (type=), number of rows (nrow=) and columns (ncol=), its specifications (free=) and starting values (values=), optionally labels (labels=), upper (ubound=) and lower (lbound=) bounds, and a name (name=).
mxMatrix( type="Full", nrow=3, ncol=1, values=c(1,2,3), name='A' )
The matrix A can be stored as the R object A by assigning the MxMatrix to the string A
A <- mxMatrix( type="Full", nrow=3, ncol=1, values=c(1,2,3), name='A' )
or we can include the mxMatrix statement as an argument in an mxModel - here saved as the R object exercise and given the name declare matrices - together with other matrices and possibly calculations based on those matrices using mxAlgebra statements. All the arguments of an mxModel are separated by a comma. The model below contains just two matrices, A and B. Note that the last argument is not followed by a comma, but rather by an right bracket to close the mxModel.
exercise <- mxModel("declare matrices", mxMatrix( type="Full", nrow=3, ncol=1, values=c(1,2,3), name='A' ), mxMatrix( type="Full", nrow=3, ncol=1, values=c(1,2,3), name='B' ) )
Assume we want to calculate the (q1) the sum of the matrices A and B, (q2) the element by element multiplication (Dot product) of A and B, (q3) the transpose of matrix A, and the (q4) outer and (q5) inner products of the matrix A, using regular matrix multiplication, i.e.:
we invoke the mxAlgebra command which performs an algebra operation between previously declared matrices, which are all included within an mxModel. Note that in R, transpose is represented by t(), regular matrix multiplication \%*\% and dot multiplication as *. We also assign the algebras a name to refer back to them later:
mxAlgebra( expression=A + B, name='q1' ) mxAlgebra( expression=A * A, name='q2' ) mxAlgebra( expression=t(A), name='q3' ) mxAlgebra( expression=A %*% t(A), name='q4' ) mxAlgebra( expression=t(A) %*% A, name='q5' )
For the algebras to be evaluated, they become arguments of the mxModel command, as do the declared matrices, each separated by comma’s. The model, which is here saved as the R object algebraExercises and given the name perform algebra on matrices, is then executed by the mxRun command, as shown in the full code below, saved in an R file matrixAlgebra.R:
require(OpenMx) algebraExercises <- mxModel("perform algebra on matrices", mxMatrix(type="Full", nrow=3, ncol=1, values=c(1,2,3), name='A'), mxMatrix(type="Full", nrow=3, ncol=1, values=c(1,2,3), name='B'), mxAlgebra(expression=A+B, name='q1'), mxAlgebra(expression=A*A, name='q2'), mxAlgebra(expression=t(A), name='q3'), mxAlgebra(expression=A%*%t(A), name='q4'), mxAlgebra(expression=t(A)%*%A, name='q5') ) answers <- mxRun(algebraExercises) answers@algebras result <- mxEval(list(q1,q2,q3,q4,q5),answers)
The resulting R object answers from running the OpenMx script algebraExercises contains the same matrices and algebras with the values in the algebras being the result of the calculations on the matrices.
As you notice, we added some lines at the end to generate the desired output. As the resulting matrices and algebras are stored in answers; we can refer back to them by specifying answers@matrices or answers@algebras. We can also calculate any additional quantities or perform extra matrix operations on the results using the mxEval command. For example, if we want to see a list of all the answers to the questions in matrixAlgebra.R, the results would look like this:
[] [,1] [1,] 2 [2,] 4 [3,] 6 [] [,1] [1,] 1 [2,] 4 [3,] 9 [] [,1] [,2] [,3] [1,] 1 2 3 [] [,1] [,2] [,3] [1,] 1 2 3 [2,] 2 4 6 [3,] 3 6 9 [] [,1] [1,] 14
So far, we have introduced five new commands: mxMatrix, mxAlgebra, mxModel, mxRun and mxEval. These commands allow us to run a wide range of jobs, from simple matrix algebra to rather complicated SEM models. Let’s move to an example involving optimizing the likelihood of observed data.
When collecting data to test a specific hypothesis, one of the first things one typically does is to check the basic descriptive statistics, such as the means, variances and covariances/correlations. We can use basic functions in R, i.e., summary(Data) - or the alternative describe(Data) from the package psych - meanCol(Data), cov(Data) or cor(Data) to perform these operations. We can even get R to provide significance levels for the correlations etc. However, if we want to test specific hypotheses about the data by maximum likelihood (ML), for example, test whether the correlation between two variables is significantly different from zero, we need to compare the likelihood of the data when the correlation is freely estimated with the likelihood of the data when the correlation is fixed to zero. Let’s work through a specific example.
Say, we have collected data on two variables X and Y in 1000 individuals, and R descriptive statistics has shown that the correlation between them is 0.5. For the sake of this example, we used another built-in function in the R package MASS, namely mvrnorm, to generate multivariate normal data for 1000 individuals with means of 0.0, variances of 1.0 and a correlation (rs) of 0.5 between X and Y. Note that the first argument of mvrnorm is the sample size, the second the vector of means, and the third the covariance matrix to be simulated. We save the data in the object xy and create a vector of labels for the two selected variables, hence selVars, which is used in the dimnames statement later on. The dimnames` are expected to be a list with rows corresponding to subjects and columns to variables. The rows are typically not labeled thus we use NULL, the columns get the labels of the variables from selVars. The R functions summary() and cov() are used to verify that the simulations appear OK.
#Simulate Data require(MASS) set.seed(200) rs=.5 xy <- mvrnorm (1000, c(0,0), matrix(c(1,rs,rs,1),2,2)) testData <- xy selVars <- c('X','Y') dimnames(testData) <- list(NULL, selVars) summary(testData) cov(testData)
To evaluate the likelihood of a set of correlated data using SEM, we estimate a saturated model with free means, free variances and a covariance. It is called a saturated model as estimated as many parameters as there are observed statistics (including both means and (co)variances). Let’s start with specifying the mean vector. We use the mxMatrix command, provide the type, here Full, the number of rows and columns (nnow= and ncol=), respectively 1 and 2, the specification of free/fixed parameters (free=T/F), the starting values (values=), and a name. Given all the elements of this 1x2 matrix are free, we can use free=TRUE. The starting values are provided using a list, i.e. c(0,0). Finally, we are explicit in naming this matrix expMean. Thus the matrix command looks like this. Note the soft tabs to improve readability.
bivCorModel <- mxModel("bivCor", mxMatrix( type="Full", nrow=1, ncol=2, free=TRUE, values=c(0,0), name="expMean" ),
Next, we need to specify the expected covariance matrix. As this matrix is symmetric, we could estimate it directly as a symmetric matrix. However, to avoid solutions that are not positive definite (and get us into optimization trouble), we will use a Cholesky decomposition. Thus, we specify a lower triangular matrix (matrix with free elements on the diagonal and below the diagonal, and zero’s above the diagonal), and multiply it with its transpose to generate a symmetric matrix. We will use a mxMatrix command to specify the lower triangular matrix and a mxAlgebra command to set up the symmetric matrix. The matrix is a 2x2 free lower matrix with starting values of 0.5 and the name “Chol”. We can now refer back to this matrix by its name in the mxAlgebra statement. We use a regular multiplication of Chol with its transpose t(Chol), and name this as expCov. Note that we do not directly estimate the two variances and one covariance, but rather the three elements of a lower triangle of a matrix of the same size. The number of elements in the lower triangle of a matrix are always the same as the number of elements in a symmetric matrix.
mxMatrix( type="Lower", nrow=2, ncol=2, free=TRUE, values=0.5, name="Chol" ), mxAlgebra( expression=Chol %*% t(Chol), name="expCov" ),
Now that we have specified our ‘model’, we need to supply the data. This is done with the mxData command. The first argument includes the actual data, in the type given by the second argument. Type can be a covariance matrix (cov), a correlation matrix (cor), a matrix of cross-products (sscp) or raw data (raw). We will use the latter option and read in the raw data directly from the simulated dataset testData.
mxData( observed=testData, type="raw" ),
Next, we specify which objective function we wish to use to obtain the likelihood of the data. Given we fit to the raw data, we use the full information maximum likelihood (FIML) objective function mxFIMLObjective. Its arguments are the expected covariance matrix (covariance=), generated using the mxMatrix and mxAlgebra commands as expCov, and the expected means vector (means=), generated using the mxMatrix command as expMeans, and dimnames. The dimnames are a type of label that is required to recognize the expected mean vector and expected covariance matrix and match up the rows and columns of the model with those of the data. For a mean vector, the first element is always NULL given mean vectors always have one row. The second element of the list should have the labels for the two variables c('X','Y') which we have previously assigned to the object selVars. For a covariance matrix, both elements are the labels for the two variables, thus selVars. Given the key piece of information for the columns of the mean vector and the rows and columns of the covariance matrix is the list of variables, that is the only element required for dimnames.
mxFIMLObjective( covariance="expCov", means="expMean", dimnames=selVars) )
All these elements become arguments of the mxModel command, seperated by comma’s. The first argument can be a name, as in this case “bivCor” or another model (see below). The model is saved in an R object ‘bivCorModel’. This MxModel object - note the capital M in MxModel for the resulting R object compared to the lower case m in mxModel for the command - becomes the argument of the mxRun command, which evaluates the model and provides output - if the model ran successfully - using the following command. Note that we have shrunk every command to one line to provide a better overview of the script here.
bivCorModel <- mxModel("bivCor", mxMatrix( type="Full", nrow=1, ncol=2, free=TRUE, values=c(0,0), name="expMean" ), mxMatrix( type="Lower", nrow=2, ncol=2, free=TRUE, values=0.5, name="Chol" ), mxAlgebra( expression=Chol %*% t(Chol), name="expCov", ), mxData( observed=testData, type="raw" ), mxFIMLObjective( covariance="expCov", means="expMean", dimnames=selVars) ) bivCorFit <- mxRun(bivCorModel)
We can request various final values of the output using summary(bivCorFit) or the mxEval command. In the following example, the simplest use case of mxEval is seen. The name of a matrix or algebra is used, and mxEval returns the current value of that matrix or algebra. See the OpenMx Style Guide for more advanced uses of the mxEval command.
EM <- mxEval(expMean, bivCorFit) EC <- mxEval(expCov, bivCorFit) LL <- mxEval(objective,bivCorFit)
These commands generate the following output:
EM X Y [1,] 0.03211646 -0.004883803 EC X Y X 1.0092847 0.4813501 Y 0.4813501 0.9935387 LL [,1] [1,] 5415.772
Standard lists of data summary, parameter estimates and goodness-of-fit statistics can be obtained with the summary command.:
> summary(bivCorFit) X Y Min. :-2.942561 Min. :-3.296159 1st Qu.:-0.633711 1st Qu.:-0.596177 Median :-0.004139 Median :-0.010538 Mean : 0.032116 Mean :-0.004884 3rd Qu.: 0.739236 3rd Qu.: 0.598326 Max. : 4.173841 Max. : 4.006771 name matrix row col parameter estimate error estimate 1 <NA> expMean 1 1 0.032116456 0.02228409 2 <NA> expMean 1 2 -0.004883803 0.02235021 3 <NA> Chol 1 1 1.004631642 0.01575904 4 <NA> Chol 2 1 0.479130899 0.02099642 5 <NA> Chol 2 2 0.874055066 0.01376876 Observed statistics: 2000 Estimated parameters: 5 Degrees of freedom: 1995 -2 log likelihood: 5415.772 Saturated -2 log likelihood: Chi-Square: p: AIC (Mx): 1425.772 BIC (Mx): -4182.6 adjusted BIC: RMSEA: 0
If we want to test whether the covariance/correlation between X and Y is significantly different from zero, we could fit a submodel and compare it with the previous saturated model. Given that this model is essentially the same as the original, except for the covariance, we create a new mxModel (named bivCorModelSub) with as first argument the old model (named bivCorModel). Then we only have to specify the matrix that needs to be changed, in this case the lower triangular matrix becomes essentially a diagonal matrix, obtained by fixing the off-diagonal elements to zero in the free and values arguments
#Test for Covariance=Zero bivCorModelSub <-mxModel(bivCorModel, mxMatrix( type="Diag", nrow=2, ncol=2, free=TRUE, name="Chol" ))
Or we can write it more succintly as follows:
bivCorModelSub <-mxModel(bivCorModel, mxMatrix( type="Diag", nrow=2, ncol=2, free=TRUE, name="Chol" )) bivCorFitSub <- mxRun(bivCorModelSub)
We can output the same information as for the saturated job, namely the expected means and covariance matrix and the likelihood, and then use R to calculate other statistics, such as the Chi-square goodness-of-fit.
EMs <- mxEval(expMean, bivCorFitSub) ECs <- mxEval(expCov, bivCorFitSub) LLs <- mxEval(objective, bivCorFitSub) Chi= LLs-LL; LRT= rbind(LL,LLs,Chi); LRT
Now that you have seen the basics of OpenMx, let us walk through an example in more detail. We decided to use a twin model example for several reasons. Even though you may not have any background in behavior genetics or genetic epidemiology, the example illustrates a number of features you are likely to encounter at some stage. We will present the example in two ways: (i) path analysis representation, and (ii) matrix algebra representation. Both give exactly the same answer, so you can choose either one or both to get some familiarity with the two approaches.
We will not go into detail about the theory of this model, as that has been done elsewhere (refs). Briefly, twin studies rely on comparing the similarity of identical (monozygotic, MZ) and fraternal (dizygotic, DZ) twins to infer the role of genetic and environmental factors on individual differences. As MZ twins have identical genotypes, similarity between MZ twins is a function of shared genes, and shared environmental factors. Similarity between DZ twins is a function of some shared genes (on average they share 50% of their genes) and shared environmental factors. A basic assumption of the classical twin study is that the MZ and DZ twins share environmental factors to the same extent.
The basic model typically fit to twin data from MZ and DZ twins reared together includes three sources of latent variables: additive genetic factors (A), shared environmental influences (C) and unique environmental factors (E), We can estimate these three sources of variance from the observed variances, the MZ and the DZ covariance. The expected variance is the sum of the three variance components (A + C + E). The expected covariance for MZ twins is (A + C) and that of DZ twins is (.5A + C). As MZ and DZ twins have different expected covariances, we have a multiple group model.
It has been standard in twin modeling to fit models to the raw data, as often data are missing on some co-twins. When using FIML, we also need to specify the expected means. There is no reason to expect that the variances are different for twin 1 and twin 2, neither are the means for twin 1 and twin 2 expected to differ. This can easily be verified by fitting submodels to the saturated model, prior to fitting the ACE model.
Let us start by simulating twin data followed by fitting a series of models. The code includes both the twin data simulation and several OpenMx scripts to analyze the data. We will describe each of the parts in turn and include the code for the specific part in the code blocks. Note that a more extensive example is discussed later in Genetic Epidemiology, Matrix Specification.
First, we simulate twin data using the mvrnorm R function. If the additive genetic factors (A) account for 50% of the total variance and the shared environmental factors (C) for 30%, thus leaving 20% explained by specific environmental factors (E), then the expected MZ twin correlation is a^2 + c^2 or 0.8 in this case, and the expected DZ twin correlation is 0.55, calculated as .5*a^2 + c^2. We simulate 1000 pairs of MZ and DZ twins each with zero means and a correlation matrix according to the values listed above. We run some basic descriptive statistics on the simulated data, using regular R functions.
require(OpenMx) require(psych) require(MASS) set.seed(200) a2<-0.5 #Additive genetic variance component (a squared) c2<-0.3 #Common environment variance component (c squared) e2<-0.2 #Specific environment variance component (e squared) rMZ <- a2+c2 rDZ <- .5*a2+c2 DataMZ <- mvrnorm (1000, c(0,0), matrix(c(1,rMZ,rMZ,1),2,2)) DataDZ <- mvrnorm (1000, c(0,0), matrix(c(1,rDZ,rDZ,1),2,2)) selVars <- c('t1','t2') colnames(DataMZ) <- selVars colnames(DataDZ) <- selVars describe(DataMZ) describe(DataDZ) colMeans(DataMZ,na.rm=TRUE) colMeans(DataDZ,na.rm=TRUE) cov(DataMZ,use="complete") cov(DataDZ,use="complete")
We typically start with fitting a saturated model, estimating means, variances and covariances separately by order of the twins (twin 1 vs twin 2) and by zygosity (MZ vs DZ pairs), to establish the likelihood of the data. This is essentially similar to the optimization script discussed above, except that we now have two variables (same variable for twin 1 and twin 2) and two groups (MZ and DZ). Thus, the saturated model will have two matrices for the expected means of MZs and DZs, and two for the expected covariances, generated from multiplying a lower triangular matrix with its transpose, one for each group. The raw data are read in using the mxData command, and the corresponding objective function mxFIMLObjective applied.
mxModel("MZ", mxMatrix( type="Full", nrow=1, ncol=2, free=TRUE, values=c(0,0), name="expMeanMZ"), mxMatrix( type="Lower", nrow=2, ncol=2, free=TRUE, values=.5, name="CholMZ"), mxAlgebra( expression=CholMZ %*% t(CholMZ), name="expCovMZ"), mxData( observed=DataMZ, type="raw"), mxFIMLObjective( covariance="expCovMZ", means="expMeanMZ", dimnames=selVars) )
Note that the mxModel statement for the DZ twins is almost identical to that for MZ twins, except for the names of the objects and data. If the arguments to the OpenMx command are given in the default order (see i.e. ?mxMatrix to open the help/reference page for that command), then it is not necessary to include the name of the argument. Given we skip a few optional arguments, such as lbound and ubound, the argument name name= is included to refer to the right argument. For didactic purposes, we prefer the formatting used for the MZ group, with soft tabs and each argument on a separate line, etc. (see list of formatting rules). However, the experienced user may want to use a more compact form, as the one used for the DZ group.
mxModel("DZ", mxMatrix("Full", 1, 2, T, c(0,0), name="expMeanDZ"), mxMatrix("Lower", 2, 2, T, .5, name="CholDZ"), mxAlgebra(CholDZ %*% t(CholDZ), name="expCovDZ"), mxData(DataDZ, "raw"), mxFIMLObjective("expCovDZ", "expMeanDZ", selVars))
The two models are then combined in a ‘super’model which includes them as arguments. Additional arguments are an mxAlgebra statement to add the objective funtions/likelihood of the two submodels. To evaluate them simultaneously, we use the mxAlgebraObjective with the previous algebra as its argument. The mxRun command is used to start optimization.
twinSatModel <- mxModel("twinSat", mxModel("MZ", mxMatrix("Full", 1, 2, T, c(0,0), name="expMeanMZ"), mxMatrix("Lower", 2, 2, T, .5, name="CholMZ"), mxAlgebra(CholMZ %*% t(CholMZ), name="expCovMZ"), mxData(DataMZ, type="raw"), mxFIMLObjective("expCovMZ", "expMeanMZ", selVars)), mxModel("DZ", mxMatrix("Full", 1, 2, T, c(0,0), name="expMeanDZ"), mxMatrix("Lower", 2, 2, T, .5, name="CholDZ"), mxAlgebra(CholDZ %*% t(CholDZ), name="expCovDZ"), mxData(DataDZ, type="raw"), mxFIMLObjective("expCovDZ", "expMeanDZ", selVars)), mxAlgebra(MZ.objective + DZ.objective, name="minus2loglikelihood"), mxAlgebraObjective("minus2loglikelihood") ) twinSatFit <- mxRun(twinSatModel)
It is always helpful/advised to check the model specifications before interpreting the output. Here we are interested in the values for the expected mean vectors and covariance matrices, and the goodness-of-fit statistics, including the likelihood, degrees of freedom, and any other derived indices, such as i.e. Akaike’s Information Criterion, which can be obtained by summary(twinSatFit).
ExpMeanMZ <- mxEval(MZ.expMeanMZ, twinSatFit) ExpCovMZ <- mxEval(MZ.expCovMZ, twinSatFit) ExpMeanDZ <- mxEval(DZ.expMeanDZ, twinSatFit) ExpCovDZ <- mxEval(DZ.expCovDZ, twinSatFit) LL_Sat <- mxEval(objective, twinSatFit)
Before we move on to fit the ACE model to the same data, we may want to test some of the assumptions of the twin model, i.e. that the means and variances are the same for twin 1 and twin 2, and that they are the same for MZ and DZ twins. This can be done as an omnibus test, or stepwise. Let us start by equating the means for both twins, separately in the two groups. We accomplish this by using the same label (just one label which will be reused by R) for the two free parameters for the means per group. As the majority of the previous script stays the same, we start by copying the old model into a new one. We then include the arguments of the model that require a change.
twinSatModelSub1 <- mxModel(twinSatModel, name = "twinSatSub1") twinSatModelSub1$MZ$expMeanMZ <- mxMatrix("Full", 1, 2, TRUE, 0, "mMZ") twinSatModelSub1$MZ$expMeanMZ <- mxMatrix("Full", 1, 2, TRUE, 0, "mDZ") twinSatFitSub1 <- mxRun(twinSatModelSub1)
If we want to test if we can equate both means across twin order and zygosity at once, we will end up with the following specification. Note that we use the same label across models for elements that need to be equated.
twinSatModelSub2 <- mxModel(twinSatModelSub1, name = "twinSatSub2") twinSatModelSub2$MZ$expMeanMZ <- mxMatrix("Full", 1, 2, TRUE, 0, "mean") twinSatModelSub2$DZ$expMeanDZ <- mxMatrix("Full", 1, 2, TRUE, 0, "mean") twinSatFitSub2 <- mxRun(twinSatModelSub2)
We can compare the likelihood of this submodel to that of the fully saturated model or the previous submodel using the results from mxEval commands with regular R algebra. A summary of the model parameters, estimates and goodness-of-fit statistics can also be obtained using summary(twinSatFit).
LL_Sat <- mxEval(objective, twinSatFit) LL_Sub1 <- mxEval(objective, twinSatFitSub1) LRT1 <- LL_Sub1 - LL_Sat LL_Sub2 <- mxEval(objective, twinSatFitSub1) LRT2 <- LL_Sub2 - LL_Sat
One assumption of the classical twin study is that the variances of twin 1 and twin 2 are not significantly different, nor that they differ between MZ and DZ twins. Although the principle of testing equality of variances across twin order and zygosity are the same as those of testing equality of means, in practice the test of variances are more complicated, because we do not directly estimate the variances, but rather use a Cholesky decomposition. We thus first have to use algebra to extract the expected variances and then use constraints to equate the correct elements of the expected covariance matrices. As this is an introductory chapter, we will leave that treatment for a later example and move to the latent variable model example.
Now, we are ready to specify the ACE model to test which sources of variance significantly contribute to the phenotype and estimate their best value. The structure of this script is going to mimic that of the saturated model. The main difference is that we no longer estimate the variance-covariance matrix directly, but express it as a function of the three sources of variance, A, C and E. As the same sources are used for the MZ and the DZ group, the matrices which will represent them are part of the ‘super’model. As these sources are variances, which need to be positive, we typically use a Cholesky decomposition of the standard deviations (and effectively estimate a rather then a^2, see later for more in depth coverage). Thus, we specify three separate matrices for the three sources of variance using the mxMatrix command and ‘calculate’ the variance components with the mxAlgebra command. Note that there are a variety of ways to specify this model, we have picked one that corresponds well to previous Mx code, and has some intuitive appeal.
#Specify ACE Model twinACEModel <- mxModel("twinACE", mxModel("ACE", # Matrix expMean for expected mean vector for MZ and DZ twins mxMatrix( type="Full", nrow=1, ncol=2, free=TRUE, values=20, label="mean", name="expMean"), # Matrices a, c, and e to store the a, c, and e path coefficients mxMatrix( type="Full", nrow=1, ncol=1, free=TRUE, values=.6, label="a11", name="a"), mxMatrix( type="Full", nrow=1, ncol=1, free=TRUE, values=.6, label="c11", name="c"), mxMatrix( type="Full", nrow=1, ncol=1, free=TRUE, values=.6, label="e11", name="e"), # Matrixes A, C, and E to compute A, C, and E variance components mxAlgebra( expression=a * t(a), name="A"), mxAlgebra( expression=c * t(c), name="C"), mxAlgebra( expression=e * t(e), name="E"), # Matrix expCovMZ for expected covariance matrix for MZ twins mxAlgebra( expression= rbind( cbind(A+C+E, A+C), cbind(A+C, A+C+E)), name="expCovMZ"), # Matrix expCovMZ for expected covariance matrix for DZ twins mxAlgebra( expression= rbind( cbind(A+C+E, .5%x%A+C), cbind(.5%x%A+C, A+C+E)), name="expCovDZ") ), mxModel("MZ", mxData( observed=DataMZ, type="raw"), mxFIMLObjective( covariance="ACE.expCovMZ", means="ACE.expMean", dimnames=selVars) ), mxModel("DZ", mxData( observed=DataDZ, type="raw"), mxFIMLObjective( covariance="ACE.expCovDZ", means="ACE.expMean", dimnames=selVars) ), # Algebra to combine objective function of MZ and DZ groups mxAlgebra(MZ.objective + DZ.objective, name="minus2loglikelihood"), mxAlgebraObjective("minus2loglikelihood") ) twinACEFit <- mxRun(twinACEModel)
Relevant output can be generated with print or summary statements or specific output can be requested using the mxEval command. Typically we would compare this model back to the saturated model to interpret its goodness-of-fit. Parameter estimates are obtained and can easily be standardized. A typical analysis would likely include the following output.
LL_ACE <- mxEval(objective, twinACEFit) LRT_ACE= LL_ACE - LL_Sat #Retrieve expected mean vector and expected covariance matrices MZc <- mxEval(ACE.expCovMZ, twinACEFit) DZc <- mxEval(ACE.expCovDZ, twinACEFit) M <- mxEval(ACE.expMean, twinACEFit) #Retrieve the A, C, and E variance components A <- mxEval(ACE.A, twinACEFit) C <- mxEval(ACE.C, twinACEFit) E <- mxEval(ACE.E, twinACEFit) #Calculate standardized variance components V <- (A+C+E) a2 <- A/V c2 <- C/V e2 <- E/V #Build and print reporting table with row and column names ACEest <- rbind(cbind(A,C,E),cbind(a2,c2,e2)) ACEest <- data.frame(ACEest, row.names=c("Variance Components","Standardized VC")) names(ACEest)<-c("A", "C", "E") ACEest; LL_ACE; LRT_ACE
Similarly to fitting submodels from the saturated model, we typically fit submodels of the ACE model to test the significance of the sources of variance. One example is testing the significance of shared environmental factors by dropping the free parameter for c (fixing it to zero). We call up the previous model and include the new specification for the matrix to be changed, and rerun.
twinAEModel <- mxRename(twinACEModel, "twinAE") twinAEModel$ACE.c <- mxMatrix(type="Full", nrow=1, ncol=1, free=FALSE, values=.6, label="c11", name="c") twinAEFit <- mxRun(twinAEModel)
We discuss twin analysis examples in more detail in the detailed example code. We hope we have given you some idea of the features of OpenMx.