,
Joshua N. Pritikin [ctb]| Title: | Easy Model-Builder Functions for 'OpenMx' |
|---|---|
| Description: | Utilities for building certain kinds of common matrices and models in the extended structural equation modeling package, 'OpenMx'. |
| Authors: | Michael D. Hunter [aut, cre] ,
Joshua N. Pritikin [ctb] |
| Maintainer: | Michael D. Hunter <[email protected]> |
| License: | GPL |
| Version: | 0.3-2 |
| Built: | 2025-03-08 03:30:34 UTC |
| Source: | https://bitbucket.org/mhunter/easymx |
EasyMx is a package for extended structural equation modeling. It is built as a higher-level frontend on top of OpenMx. It is intended as an Easy introduction to OpenMx: Easy Mx. Try the example below.
All of the functions in the EasyMx package create OpenMx objects. These are most often MxMatrix, MxAlgebra, or MxModel objects. The primary difference between EasyMx and OpenMx is design philosophy. OpenMx has its foundation in WYSIWID: What you say is what it does. This requires the user to be very explicit. The EasyMx package is not as strong or flexible as OpenMx, but it places less burden on the user. Many decisions are made automatically for the user. Some of them are modifiable within EasyMx; for others the user is encouraged to use OpenMx, where nearly everything is modifiable.
The package is broadly divided into two styles of functions: matrix builders and model builders.
The matrix builder functions are utilities for building common structural equation model matrices. These include emxLoadings for factor loadings, emxResiduals for residual variances, emxCovariances for latent or manifest covariances, emxMeans for means and intercepts matrices, and emxThresholds for thresholds matrices when ordinal data are involved.
The model builder functions are higher-level utilities for building common kinds of structural equation models. The model builders often call several matrix builders. The model builders include emxFactorModel for (multiple) factor models, emxGrowthModel for latent growth curve models, and emxRegressionModel for full-information likelihood estimation of regression for observed variables.
There are also a few model builder functions for non-standard structural equation models. In particular, the emxVARModel function creates vector autoregressive models, and the emxStateSpaceMixtureModel function creates state space mixture models.
A third category of functions encompasses special functions for behavior genetics modeling. Some of these functions are matrix builders, and others are model builders. The lowest-level functions for behavior genetics are emxCholeskyVariance, emxGeneticFactorVariance, emxRelatednessMatrix, and emxKroneckerVariance.
A higher-level set of behavior genetics matrix builders create all the matrices and algebraic statements needed for e.g. the A component of an ACE model. These functions are emxCholeskyComponent and emxGeneticFactorComponent.
The highest-level of behavior genetics functions builds some basic twin models. The primary function for this is emxTwinModel.
Finally, a mixture model helper is provided: emxMixtureModel.
# Make and run a one factor model ## Not run: require(EasyMx) data(demoOneFactor) fmod <- list(G=names(demoOneFactor)) fit1 <- emxFactorModel(fmod, demoOneFactor, run=TRUE) summary(fit1) ## End(Not run)# Make and run a one factor model ## Not run: require(EasyMx) data(demoOneFactor) fmod <- list(G=names(demoOneFactor)) fit1 <- emxFactorModel(fmod, demoOneFactor, run=TRUE) summary(fit1) ## End(Not run)
This function creates all the objects needed for a Cholesky component.
emxCholeskyComponent(x, xname, xvalues, xfree, h=2, hname=paste0('H', xname), hvalues, hlabels)emxCholeskyComponent(x, xname, xvalues, xfree, h=2, hname=paste0('H', xname), hvalues, hlabels)
x |
character vector. The base names of the variables used for the matrix with no repetition for twins (x, y, z not x1, y1, z1, x2, y2, z2). |
xname |
character. Name of the component matrix. |
xvalues |
numeric vector. Values of the component matrix. |
xfree |
logical vector. Whether each element of the component matrix is freely estimates. |
h |
numeric. The number of variables for the relatedness matrix, i.e. the number of critters with relationships |
hname |
character. Name of the relatedness matrix. |
hvalues |
numeric vector. Values for the relatedness matrix. |
hlabels |
character vector. Labels for the relatedness matrix. |
This function is a combination of emxCholeskyVariance, emxRelatednessMatrix, and emxKroneckerVariance.
A list with elements (1) the lower triangular matrix for the Cholesky, (2) the full positive definite variance matrix, (3) the relatedness matrix, and (4) the Kronecker product of the variance matrix and the relatedness matrix.
# Create an ACE model in 22 lines require(EasyMx) require(OpenMx) data(twinData) twinVar = names(twinData) selVars <- c('ht1', 'bmi1','ht2','bmi2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] nVar = length(selVars)/2 x <- paste0('x', 1:nVar) acomp <- emxCholeskyComponent(x, 'A', hvalues=c(1, .5, 1), hlabels=c(NA, 'data.RCoef', NA)) ccomp <- emxCholeskyComponent(x, 'C', hvalues=c(1, 1, 1)) ecomp <- emxCholeskyComponent(x, 'E', hvalues=c(1, 0, 1)) totalVar <- mxAlgebra(AKron + CKron + EKron, 'V', dimnames=list(selVars, selVars)) totalMean <- emxMeans(selVars, type='twin') expect <- mxExpectationNormal(totalVar$name, totalMean$name) fitfun <- mxFitFunctionML() comlist <- c(acomp, ccomp, ecomp, list(totalVar, totalMean, expect, fitfun)) model <- mxModel('model', comlist, mxData(mzdzData, 'raw')) ## Not run: run2 <- mxRun(model) ## End(Not run)# Create an ACE model in 22 lines require(EasyMx) require(OpenMx) data(twinData) twinVar = names(twinData) selVars <- c('ht1', 'bmi1','ht2','bmi2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] nVar = length(selVars)/2 x <- paste0('x', 1:nVar) acomp <- emxCholeskyComponent(x, 'A', hvalues=c(1, .5, 1), hlabels=c(NA, 'data.RCoef', NA)) ccomp <- emxCholeskyComponent(x, 'C', hvalues=c(1, 1, 1)) ecomp <- emxCholeskyComponent(x, 'E', hvalues=c(1, 0, 1)) totalVar <- mxAlgebra(AKron + CKron + EKron, 'V', dimnames=list(selVars, selVars)) totalMean <- emxMeans(selVars, type='twin') expect <- mxExpectationNormal(totalVar$name, totalMean$name) fitfun <- mxFitFunctionML() comlist <- c(acomp, ccomp, ecomp, list(totalVar, totalMean, expect, fitfun)) model <- mxModel('model', comlist, mxData(mzdzData, 'raw')) ## Not run: run2 <- mxRun(model) ## End(Not run)
This function creates a Cholesky variance matrix and associated MxMatrix and MxAlgebra objects.
emxCholeskyVariance(x, name, values=.8, free=TRUE)emxCholeskyVariance(x, name, values=.8, free=TRUE)
x |
character vector. The names of the variables used for the matrix. |
name |
character. The name of the variance matrix created. |
values |
numeric vector. The starting values for the lower triangular matrix. |
free |
logical vector. Whether the lower triangular elements are free. |
This is a helper function for creating a matrix that is symmetric and positive definite. Full covariance matrices are the most common case of these. In a behavior genetics modeling context, Cholesky components can be created for Additive genetics, Common environments, and unique Environments. These are unrestrictive models of the covariances of multiple phenotypes.
A list with two components. The first component is the lower triangular MxMatrix. The second component is an MxAlgebra, the result of which is the positive definite variance matrix.
# Create a Cholesky variance matrix called 'A' require(EasyMx) nVar <- 3 x <- paste0('x', 1:nVar) amat <- emxCholeskyVariance(x, 'A')# Create a Cholesky variance matrix called 'A' require(EasyMx) nVar <- 3 x <- paste0('x', 1:nVar) amat <- emxCholeskyVariance(x, 'A')
UNDER ACTIVE DEVELOPMENT. DO NOT TRUST. This function creates all the objects needed for a Common Pathway component.
emxCommonPathwayComponent(x, xname, xvalues=.8, xfree=TRUE, xlbound=NA, xubound=NA, h=2, hname=paste0('H', xname), hvalues, hlabels)emxCommonPathwayComponent(x, xname, xvalues=.8, xfree=TRUE, xlbound=NA, xubound=NA, h=2, hname=paste0('H', xname), hvalues, hlabels)
x |
character vector. The base names of the variables used for the matrix with no repetition for twins (x, y, z not x1, y1, z1, x2, y2, z2). |
xname |
character. Name of the component matrix. |
xvalues |
Matrix of fixed and/or starting values of parameters |
xfree |
Matrix of TRUE (for free) and FALSE (for fixed) values |
xlbound |
Matrix of numeric lower bounds for parameters |
xubound |
Matrix of numeric upper bounds for parameters |
h |
numeric. The number of variables for the relatedness matrix, i.e. the number of critters with relationships |
hname |
character. Name of the relatedness matrix. |
hvalues |
numeric vector. Values for the relatedness matrix. |
hlabels |
character vector. Labels for the relatedness matrix. |
This function is a combination of emxCholeskyVariance, emxRelatednessMatrix, and emxKroneckerVariance.
A list with elements (1) the lower triangular matrix for the Cholesky, (2) the full positive definite variance matrix, (3) the relatedness matrix, and (4) the Kronecker product of the variance matrix and the relatedness matrix.
# Create an ACE model in 22 lines require(EasyMx) require(OpenMx) data(twinData) twinVar = names(twinData) selVars <- c('ht1', 'bmi1','ht2','bmi2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] nVar = length(selVars)/2 x <- paste0('x', 1:nVar) acomp <- emxCholeskyComponent(x, 'A', hvalues=c(1, .5, 1), hlabels=c(NA, 'data.RCoef', NA)) ccomp <- emxCholeskyComponent(x, 'C', hvalues=c(1, 1, 1)) ecomp <- emxCholeskyComponent(x, 'E', hvalues=c(1, 0, 1)) totalVar <- mxAlgebra(AKron + CKron + EKron, 'V', dimnames=list(selVars, selVars)) totalMean <- emxMeans(selVars, type='twin') expect <- mxExpectationNormal(totalVar$name, totalMean$name) fitfun <- mxFitFunctionML() comlist <- c(acomp, ccomp, ecomp, list(totalVar, totalMean, expect, fitfun)) model <- mxModel('model', comlist, mxData(mzdzData, 'raw')) ## Not run: run2 <- mxRun(model) ## End(Not run)# Create an ACE model in 22 lines require(EasyMx) require(OpenMx) data(twinData) twinVar = names(twinData) selVars <- c('ht1', 'bmi1','ht2','bmi2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] nVar = length(selVars)/2 x <- paste0('x', 1:nVar) acomp <- emxCholeskyComponent(x, 'A', hvalues=c(1, .5, 1), hlabels=c(NA, 'data.RCoef', NA)) ccomp <- emxCholeskyComponent(x, 'C', hvalues=c(1, 1, 1)) ecomp <- emxCholeskyComponent(x, 'E', hvalues=c(1, 0, 1)) totalVar <- mxAlgebra(AKron + CKron + EKron, 'V', dimnames=list(selVars, selVars)) totalMean <- emxMeans(selVars, type='twin') expect <- mxExpectationNormal(totalVar$name, totalMean$name) fitfun <- mxFitFunctionML() comlist <- c(acomp, ccomp, ecomp, list(totalVar, totalMean, expect, fitfun)) model <- mxModel('model', comlist, mxData(mzdzData, 'raw')) ## Not run: run2 <- mxRun(model) ## End(Not run)
This function creates a covariance matrix as an MxMatrix or MxPath object.
emxCovariances(x, values, free, path=FALSE, type, name='Variances')emxCovariances(x, values, free, path=FALSE, type, name='Variances')
x |
character vector. The names of the variables for which covariances are created. |
values |
numeric vector. See Details. |
free |
logical vector. See Details. |
path |
logical. Whether to return the MxPath object instead of the MxMatrix. |
type |
character. The kind of covariance structure to create. See Details. |
name |
The name of the matrix created. |
Possible values for the type argument are 'independent', 'full', and 'corr'. When type='independent', the remaining arguments are passes to emxResiduals. The values and free arguments are only used when the type argument is 'independent'. For all other cases, they are ignored.
When type='full', a full covariance matrix is created. That is, a symmetric matrix is created with all unique elements freely estimated. The starting values for the variances are all 1; for the covariances, all 0.5.
When type='corr', a full correlation matrix is created. That is, a symmetric matrix is created with all unique elements not on the diagonal freely estimated. The starting values for the correlations are all 0.5. The variances are fixed at 1.
Depending on the value of the path argument, either an MxMatrix or and MxPath object that can be inspected, modified, and/or included in MxModel objects.
emxFactorModel, emxGrowthModel
# Create a covariance matrix require(EasyMx) manVars <- paste0('x', 1:6) latVars <- paste0('F', 1:2) emxCovariances(manVars, type='full') emxCovariances(latVars, type='corr', path=TRUE)# Create a covariance matrix require(EasyMx) manVars <- paste0('x', 1:6) latVars <- paste0('F', 1:2) emxCovariances(manVars, type='full') emxCovariances(latVars, type='corr', path=TRUE)
This function creates a factor model as an MxModel object.
emxFactorModel(model, data, name, run=FALSE, identification, use, ordinal, ..., parameterization=c("lisrel", "ifa"), weight = as.character(NA)) emxModelFactor(model, data, name, run=FALSE, identification, use, ordinal, ..., parameterization=c("lisrel", "ifa"), weight = as.character(NA))emxFactorModel(model, data, name, run=FALSE, identification, use, ordinal, ..., parameterization=c("lisrel", "ifa"), weight = as.character(NA)) emxModelFactor(model, data, name, run=FALSE, identification, use, ordinal, ..., parameterization=c("lisrel", "ifa"), weight = as.character(NA))
model |
named list. Gives the factor loading pattern. See Details. |
data |
data used for the model |
name |
character. Optional name of the model created. |
run |
logical. Whether to run the model before returning. |
identification |
Not yet implemented. How the model is identified. Currently ignored. |
use |
character vector. The names of the variables to use. |
ordinal |
character vector. The names of the ordinal variables. |
... |
Force later arguments to be named. ... is ignored. |
parameterization |
character. Whether to specify the model as a LISREL SEM or as an Item Factor Analysis |
weight |
character. Name of the data column used for sample weights. |
The model argument must be a named list. The names of the list give the names of the latent variables. Each list element gives the names of the variables that load onto that latent variable. This may sound complicated, but the example below makes this more clear. It is intended to be visually intuitive.
An MxModel.
# Example require(EasyMx) data(myFADataRaw) xmap <- list(F1=paste0('x', 1:6), F2=paste0('y', 1:3), F3=paste0('z', 1:3)) ## Not run: mod <- emxFactorModel(xmap, data=myFADataRaw, run=TRUE) ## End(Not run)# Example require(EasyMx) data(myFADataRaw) xmap <- list(F1=paste0('x', 1:6), F2=paste0('y', 1:3), F3=paste0('z', 1:3)) ## Not run: mod <- emxFactorModel(xmap, data=myFADataRaw, run=TRUE) ## End(Not run)
This function creates all the objects needed for Genetic Factor component
emxGeneticFactorComponent(x, xname, xvalues=.8, xfree=TRUE, xlbound=NA, xubound=NA, h=2, hname=paste0('H', xname), hvalues, hlabels)emxGeneticFactorComponent(x, xname, xvalues=.8, xfree=TRUE, xlbound=NA, xubound=NA, h=2, hname=paste0('H', xname), hvalues, hlabels)
x |
character vector. The base names of the variables used for the matrix with no repetition for twins (x, y, z not x1, y1, z1, x2, y2, z2). |
xname |
character. Name of the component matrix. |
xvalues |
numeric vector. Values of the genetic factor loadings. |
xfree |
logical vector. Whether the genetic factor loadings are free. |
xlbound |
numeric vector. Lower bounds of the factor loadings. |
xubound |
numeric vector. Upper bounds of the factor loadings. |
h |
numeric. The number of variables for the relatedness matrix, i.e. the number of critters with relationships |
hname |
character. Name of the relatedness matrix. |
hvalues |
numeric vector. Values for the relatedness matrix. |
hlabels |
character vector. Labels for the relatedness matrix. |
This function is a combination of emxGeneticFactorVariance, emxRelatednessMatrix, and emxKroneckerVariance.
A list with elements (1) the genetic factor loadings matrix, (2) the full positive definite variance matrix, (3) the relatedness matrix, and (4) the Kronecker product of the variance matrix and the relatedness matrix.
# Create genetic factor A component for DZ twins require(EasyMx) xvars <- paste0('x', 1:4) acomp <- emxGeneticFactorComponent(xvars, 'A', hvalues=c(1, .5, 1))# Create genetic factor A component for DZ twins require(EasyMx) xvars <- paste0('x', 1:4) acomp <- emxGeneticFactorComponent(xvars, 'A', hvalues=c(1, .5, 1))
This function creates a variance matrix according to the genetic factor model
emxGeneticFactorVariance(x, name, values=.8, free=TRUE, lbound=NA, ubound=NA)emxGeneticFactorVariance(x, name, values=.8, free=TRUE, lbound=NA, ubound=NA)
x |
character vector. The names of the variables used for the matrix. |
name |
character. The name of the variance matrix created. |
values |
numeric vector. The starting values for the lower triangular matrix. |
free |
logical vector. Whether the lower triangular elements are free. |
lbound |
numeric vector. Lower bounds on free parameters. |
ubound |
numeric vector. Upper bounds on free parameters. |
A list with two components. The first component is the factor loadings matrix. The second component is an MxAlgebra, the result of which is the variance matrix implied by the factor loadings.
# Create a genetic factor variance matrix require(EasyMx) xvars <- paste0('x', 1:2) emxGeneticFactorVariance(xvars, 'D')# Create a genetic factor variance matrix require(EasyMx) xvars <- paste0('x', 1:2) emxGeneticFactorVariance(xvars, 'D')
This function creates a latent growth curve model as an MxModel object.
emxGrowthModel(model, data, name, run=FALSE, identification, use, ordinal, times) emxModelGrowth(model, data, name, run=FALSE, identification, use, ordinal, times)emxGrowthModel(model, data, name, run=FALSE, identification, use, ordinal, times) emxModelGrowth(model, data, name, run=FALSE, identification, use, ordinal, times)
model |
character or numeric. See Details. |
data |
data used for the model |
name |
character. Optional name of the model created. |
run |
logical. Whether to run the model before returning. |
identification |
Not yet implemented. How the model is identified. Currently ignored. |
use |
character vector. The names of the variables to use. |
ordinal |
character vector. The names of the ordinal variables. |
times |
optional character or numeric vector. Either the numeric times of measurement or the names of the variables in |
The model argument can be either a character or a number that tells the kind of growth curve to make. If it is a character it currently must be one of "Intercept", "Linear", "Quadratic", "Cubic", "Quartic", or "Quintic", and it produces a polynomial growth curve of the corresponding type. If it is a number, the function produces a polynomial growth curve of the corresponding order. Zero is an intercept only, one is linear, two is quadratic; and so on.
When missing, the times are assumed to start at zero and increment by one until the number of variables is completed.
An MxModel
emxFactorModel, emxGrowthModel
# Example require(EasyMx) data(myLongitudinalData) ## Not run: mod <- emxGrowthModel('Linear', data=myLongitudinalData, use=names(myLongitudinalData), run=TRUE) ## End(Not run)# Example require(EasyMx) data(myLongitudinalData) ## Not run: mod <- emxGrowthModel('Linear', data=myLongitudinalData, use=names(myLongitudinalData), run=TRUE) ## End(Not run)
UNDER ACTIVE DEVELOPMENT. DO NOT TRUST. This function creates all the objects needed for an Independent Pathway component.
emxIndependentPathwayComponent(x, xname, xvalues=.8, xfree=TRUE, xlbound=NA, xubound=NA, h=2, hname=paste0('H', xname), hvalues, hlabels)emxIndependentPathwayComponent(x, xname, xvalues=.8, xfree=TRUE, xlbound=NA, xubound=NA, h=2, hname=paste0('H', xname), hvalues, hlabels)
x |
character vector. The base names of the variables used for the matrix with no repetition for twins (x, y, z not x1, y1, z1, x2, y2, z2). |
xname |
character. Name of the component matrix. |
xvalues |
Matrix of fixed and/or starting values of parameters |
xfree |
Matrix of TRUE (for free) and FALSE (for fixed) values |
xlbound |
Matrix of numeric lower bounds for parameters |
xubound |
Matrix of numeric upper bounds for parameters |
h |
numeric. The number of variables for the relatedness matrix, i.e. the number of critters with relationships |
hname |
character. Name of the relatedness matrix. |
hvalues |
numeric vector. Values for the relatedness matrix. |
hlabels |
character vector. Labels for the relatedness matrix. |
This function is a combination of emxCholeskyVariance, emxRelatednessMatrix, and emxKroneckerVariance.
A list with elements (1) the lower triangular matrix for the Cholesky, (2) the full positive definite variance matrix, (3) the relatedness matrix, and (4) the Kronecker product of the variance matrix and the relatedness matrix.
# Create an ACE model in 22 lines require(EasyMx) require(OpenMx) data(twinData) twinVar = names(twinData) selVars <- c('ht1', 'bmi1','ht2','bmi2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] nVar = length(selVars)/2 x <- paste0('x', 1:nVar) acomp <- emxCholeskyComponent(x, 'A', hvalues=c(1, .5, 1), hlabels=c(NA, 'data.RCoef', NA)) ccomp <- emxCholeskyComponent(x, 'C', hvalues=c(1, 1, 1)) ecomp <- emxCholeskyComponent(x, 'E', hvalues=c(1, 0, 1)) totalVar <- mxAlgebra(AKron + CKron + EKron, 'V', dimnames=list(selVars, selVars)) totalMean <- emxMeans(selVars, type='twin') expect <- mxExpectationNormal(totalVar$name, totalMean$name) fitfun <- mxFitFunctionML() comlist <- c(acomp, ccomp, ecomp, list(totalVar, totalMean, expect, fitfun)) model <- mxModel('model', comlist, mxData(mzdzData, 'raw')) ## Not run: run2 <- mxRun(model) ## End(Not run)# Create an ACE model in 22 lines require(EasyMx) require(OpenMx) data(twinData) twinVar = names(twinData) selVars <- c('ht1', 'bmi1','ht2','bmi2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] nVar = length(selVars)/2 x <- paste0('x', 1:nVar) acomp <- emxCholeskyComponent(x, 'A', hvalues=c(1, .5, 1), hlabels=c(NA, 'data.RCoef', NA)) ccomp <- emxCholeskyComponent(x, 'C', hvalues=c(1, 1, 1)) ecomp <- emxCholeskyComponent(x, 'E', hvalues=c(1, 0, 1)) totalVar <- mxAlgebra(AKron + CKron + EKron, 'V', dimnames=list(selVars, selVars)) totalMean <- emxMeans(selVars, type='twin') expect <- mxExpectationNormal(totalVar$name, totalMean$name) fitfun <- mxFitFunctionML() comlist <- c(acomp, ccomp, ecomp, list(totalVar, totalMean, expect, fitfun)) model <- mxModel('model', comlist, mxData(mzdzData, 'raw')) ## Not run: run2 <- mxRun(model) ## End(Not run)
This function creates the wide format variance matrix when combined with a relatedness matrix
emxKroneckerVariance(h, v, name)emxKroneckerVariance(h, v, name)
h |
MxMatrix. Left hand side of the Kronecker product. Typically the relatedness matrix. |
v |
MxMatrix. Right hand side of the Kronecker product. Typically the variance matrix. |
name |
character. Name of the resulting large matrix. |
In many behavior genetic models, a relationship matrix is combined with a base variance matrix. The combination is done with a Kronecker product so that the variance exists (possibly weighted by zero or another number) for each member of the relationship.
MxAlgebra
# Create a loadings matrix require(EasyMx) x <- letters[23:26] amat <- emxCholeskyVariance(x, 'A') ahmat <- emxRelatednessMatrix(2, c(1, .5, 1), name='AH') ab <- emxKroneckerVariance(ahmat, amat[[2]], 'AB')# Create a loadings matrix require(EasyMx) x <- letters[23:26] amat <- emxCholeskyVariance(x, 'A') ahmat <- emxRelatednessMatrix(2, c(1, .5, 1), name='AH') ab <- emxKroneckerVariance(ahmat, amat[[2]], 'AB')
This function creates a factor loadings matrix as an MxMatrix or MxPath object.
emxLoadings(x, values=.8, free=TRUE, path=FALSE)emxLoadings(x, values=.8, free=TRUE, path=FALSE)
x |
named list. Gives the factor loading pattern. See Details. |
values |
numeric vector. The starting values for the nonzero loadings. |
free |
logical vector. Whether the nonzero loadings are free. |
path |
logical. Whether to return the MxPath object instead of the MxMatrix. |
The x argument must be a named list. The names of the list give the names of the latent variables. Each list element gives the names of the variables that load onto that latent variable. This may sound complicated, but the example below makes this more clear. It is intended to be visually intuitive.
Depending on the value of the path argument, either an MxMatrix or and MxPath object that can be inspected, modified, and/or included in MxModel objects.
# Create a loadings matrix require(EasyMx) xmap <- list(F1=paste0('x', 1:6), F2=paste0('y', 1:3), F3=paste0('z', 1:3)) emxLoadings(xmap) emxLoadings(xmap, path=TRUE)# Create a loadings matrix require(EasyMx) xmap <- list(F1=paste0('x', 1:6), F2=paste0('y', 1:3), F3=paste0('z', 1:3)) emxLoadings(xmap) emxLoadings(xmap, path=TRUE)
This function creates a means matrix as an MxMatrix or MxPath object.
emxMeans(x, values=0, free=TRUE, path=FALSE, type='saturated', name, column=TRUE, labels)emxMeans(x, values=0, free=TRUE, path=FALSE, type='saturated', name, column=TRUE, labels)
x |
character vector. The names of the variables for which means are created. |
values |
numeric vector. See Details. |
free |
logical vector. See Details. |
path |
logical. Whether to return the MxPath object instead of the MxMatrix. |
type |
character. The kind of covariance structure to create. See Details. |
name |
The name of the matrix created. |
column |
logical. Whether to create the means vector as a column or row. |
labels |
character vector. Optional labels for the means. |
Possible values for the type argument are 'saturated', 'equal', 'twin', 'special'.
Depending on the value of the path argument, either an MxMatrix or and MxPath object that can be inspected, modified, and/or included in MxModel objects.
emxFactorModel, emxGrowthModel
# Create a covariance matrix require(EasyMx) manVars <- paste0('x', 1:6) emxMeans(manVars, type='saturated')# Create a covariance matrix require(EasyMx) manVars <- paste0('x', 1:6) emxMeans(manVars, type='saturated')
This function creates a mxiture model as an MxModel object.
emxMixtureModel(model, data, run=FALSE, p=NA, ...) emxModelMixture(model, data, run=FALSE, p=NA, ...)emxMixtureModel(model, data, run=FALSE, p=NA, ...) emxModelMixture(model, data, run=FALSE, p=NA, ...)
model |
list. The MxModel objects that compose the mixture. |
data |
data used for the model |
run |
logical. Whether to run the model before returning. |
p |
character. Optional name of the mixing proportions matrix. |
... |
Further Mx Objects passed into the mixture model. |
The model argument is list of MxModel objects. These are the classes over which the mixture model operates.
The p argument is optional. If not specified, the function will create and properly scale the mixing proportions for you. If specified, the Mx Object that gives the mixing proportions should be a column vector (one-column matrix).
An MxModel.
# Factor Mixture Example require(EasyMx) data(myFADataRaw) xmap1 <- list(F1=paste0('x', 1:6), F2=paste0('y', 1:3), F3=paste0('z', 1:3)) mod1 <- emxFactorModel(xmap1, data=myFADataRaw, name='m1') xmap2 <- list(F1=c(paste0('x', 1:6), paste0('y', 1:3), paste0('z', 1:3))) mod2 <- emxFactorModel(xmap2, data=myFADataRaw, name='m2') mod <- emxMixtureModel(list(mod1, mod2), data=myFADataRaw) # To estimate parameters either # 1. mod <- mxRun(mod) or # 2. include run=TRUE in the arguments above summary(mod) coef(mod) # Latent Profile Example require(EasyMx) m1 <- omxSaturatedModel(demoOneFactor)[[1]] m1 <- mxRename(m1, 'profile1') m2 <- omxSaturatedModel(demoOneFactor)[[1]] m2 <- mxRename(m2, 'profile2') mod <- emxMixtureModel(list(m1, m2), data=demoOneFactor) # To estimate parameters either # 1. mod <- mxRun(mod) or # 2. include run=TRUE in the arguments above summary(mod) coef(mod) mxGetExpected(mod$profile1, 'covariance') mxGetExpected(mod$profile1, 'means') mxGetExpected(mod$profile2, 'covariance') mxGetExpected(mod$profile2, 'means')# Factor Mixture Example require(EasyMx) data(myFADataRaw) xmap1 <- list(F1=paste0('x', 1:6), F2=paste0('y', 1:3), F3=paste0('z', 1:3)) mod1 <- emxFactorModel(xmap1, data=myFADataRaw, name='m1') xmap2 <- list(F1=c(paste0('x', 1:6), paste0('y', 1:3), paste0('z', 1:3))) mod2 <- emxFactorModel(xmap2, data=myFADataRaw, name='m2') mod <- emxMixtureModel(list(mod1, mod2), data=myFADataRaw) # To estimate parameters either # 1. mod <- mxRun(mod) or # 2. include run=TRUE in the arguments above summary(mod) coef(mod) # Latent Profile Example require(EasyMx) m1 <- omxSaturatedModel(demoOneFactor)[[1]] m1 <- mxRename(m1, 'profile1') m2 <- omxSaturatedModel(demoOneFactor)[[1]] m2 <- mxRename(m2, 'profile2') mod <- emxMixtureModel(list(m1, m2), data=demoOneFactor) # To estimate parameters either # 1. mod <- mxRun(mod) or # 2. include run=TRUE in the arguments above summary(mod) coef(mod) mxGetExpected(mod$profile1, 'covariance') mxGetExpected(mod$profile1, 'means') mxGetExpected(mod$profile2, 'covariance') mxGetExpected(mod$profile2, 'means')
This function creates a separate model for every ID in a dataset, optionally fits it, and finally returns a list of models for each ID.
emxModelByID(model, data, name, run=FALSE, ID, ...)emxModelByID(model, data, name, run=FALSE, ID, ...)
model |
MxModel object. A separate model with this structure will be made and optionally fit for each ID |
data |
data used for the model. A list of data.frame objects or a single data.frame with an ID variable |
name |
character. Optional name of the model created. The individual models have this name pasted together with their ID value. |
run |
logical. Whether to run the model before returning. |
ID |
character. Name of variable that identifies each unique person. |
... |
Force later arguments to be named. ... is ignored. |
The original intent of this function is to facillitate person-specific modeling of multisubject time series data. However, it is built with sufficient generality to allow many other uses. Any model you want to fit separately by some ID variable can be split and fit with this function.
The model can be any model built by EasyMx or OpenMx. Time series models, factor models, full structural equation models, and behavior genetics models are a few relevant possbilities.
The ID variable could be a person ID, a group ID, a family ID, a country ID, and so on. Thus, this function can be useful for several types of invariance testing that start by allowing all parameters across a grouping variable to be distinct. Similarly, this function can be useful as a comparison to multilevel modeling. Instead of allowing parameters to vary across the ID variable according to a distribution, this function estimates all parameters separately.
A list of MxModel objects.
emxStateSpaceMixtureClassify , emxMixtureModel
# Example require(EasyMx) data(myFADataRaw) ds0 <- myFADataRaw[,1:3] # Make a VAR Model vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel') # Pretend you have a data set of 10 people # each measured 50 times on 3 variables ds1 <- myFADataRaw[, 1:3] ds1$id <- rep(1:10, each=nrow(myFADataRaw)/10) ## Not run: # Make and fit the state space mixture model pmod <- emxModelByID(model=vm, data=ds1, ID='id', run=TRUE) sapply(pmod, coef) # person-specific model parameters ## End(Not run)# Example require(EasyMx) data(myFADataRaw) ds0 <- myFADataRaw[,1:3] # Make a VAR Model vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel') # Pretend you have a data set of 10 people # each measured 50 times on 3 variables ds1 <- myFADataRaw[, 1:3] ds1$id <- rep(1:10, each=nrow(myFADataRaw)/10) ## Not run: # Make and fit the state space mixture model pmod <- emxModelByID(model=vm, data=ds1, ID='id', run=TRUE) sapply(pmod, coef) # person-specific model parameters ## End(Not run)
This function creates a regression model as an MxModel object.
emxRegressionModel(model, data, type='Steven', run, ...) emxModelRegression(model, data, type='Steven', run, ...)emxRegressionModel(model, data, type='Steven', run, ...) emxModelRegression(model, data, type='Steven', run, ...)
model |
formula. See Details. |
data |
data used for the model |
run |
logical. Whether to run the model before returning. |
type |
character. Either 'Steven' or 'Joshua'. See Details. |
... |
Further named arguments to be passed to |
The model argument is a formula identical to what is used in lm.
The type argument switches the kind of regression model that is specified. When there are no missing data, the two versions will estimate the same regression parameters but type='Steven' will estimate addition parameters that are not estimated by type='Joshua'. The type='Steven' model is due to Steven Boker and many others. It estimates more parameters than a typical regression analysis and has a different set of assumptions. More exactly, type='Steven' models the outcome and all of the predictors as a multivariate Normal distribution. By contrast, type='Joshua' is due to Joshua Pritikin and exactly replicates the typical regression model with its usual assumptions. In particular, type='Joshua' models the regression residual as a univariate Normal distribution. Predictors are assumed to have no measurement error (see Westfall & Yarkoni, 2016).
The benefit of type='Steven' is that it handles missing data with
full-information maximum likelihood (FIML; Enders & Bandalos, 2001), at the cost of using a different model with different assumptions from ordinary least squares regression. The benefit of type='Joshua' is that it exactly replicates regression as a maximum likelhood model, at the cost of having the same weakness in terms of missing data as OLS regression.
An MxModel.
Enders, C. K. & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. <i>Structural Equation Modeling, 8</i>(3), 430-457.
Westfall, J. & Yarkoni, T. (2016). Statistically controlling for confounding constructs is harder than you think. <i>PLoS ONE, 11</i>(3). doi:10.1371/journal.pone.0152719
# Example require(EasyMx) data(myRegDataRaw) myrdr <- myRegDataRaw myrdr[1, 4] <- NA ## Not run: run <- emxRegressionModel(y~1+x*z, data=myrdr, run=TRUE) summary(run) ## End(Not run) summary(lm(y~1+x*z, data=myrdr))# Example require(EasyMx) data(myRegDataRaw) myrdr <- myRegDataRaw myrdr[1, 4] <- NA ## Not run: run <- emxRegressionModel(y~1+x*z, data=myrdr, run=TRUE) summary(run) ## End(Not run) summary(lm(y~1+x*z, data=myrdr))
This function creates a relatedness matrix as an MxMatrix, often used in behavior genetics modeling.
emxRelatednessMatrix(nvar, values, labels, name='h')emxRelatednessMatrix(nvar, values, labels, name='h')
nvar |
numeric. The number of variables for the matrix, i.e. the number of rows or columns. |
values |
numeric vector. Values used in the matrix. |
labels |
character vector. Labels of the elements in the matrix. See Details. |
name |
character. The name of the matrix created. |
The labels argument can be used to create a "definition variable" which populates the value from one of the data columns for each row in the data. In this context, if the genetic relatedness coefficient between a pair of individuals is given by a column in the data then that information can be used to create in the relatedness matrix. Alternatively, multiple groups can be created
An MxMatrix object.
# Create a Cholesky variance matrix called 'A' require(EasyMx) ahmat <- emxRelatednessMatrix(2, c(1, .5, 1), labels=c(NA, 'data.RCoef', NA), name='AH') # data.RCoef creates a definition variable and ignores the .5 value.# Create a Cholesky variance matrix called 'A' require(EasyMx) ahmat <- emxRelatednessMatrix(2, c(1, .5, 1), labels=c(NA, 'data.RCoef', NA), name='AH') # data.RCoef creates a definition variable and ignores the .5 value.
This function creates a factor loadings matrix as an MxMatrix or MxPath object.
emxResiduals(x, values=.2, free=TRUE, lbound=NA, ubound=NA, path=FALSE, type='unique')emxResiduals(x, values=.2, free=TRUE, lbound=NA, ubound=NA, path=FALSE, type='unique')
x |
character vector. The names of the variables for which residual variances are created. |
values |
numeric vector. The starting values for the variances. |
free |
logical vector. Whether the variances are free. |
lbound |
numeric vector. Lower bounds for the variances. |
ubound |
numeric vector. Upper bounds for the variances. |
path |
logical. Whether to return the MxPath object instead of the MxMatrix. |
type |
character. The kind of residual variance structure to create. See Details. |
Possible values for the type argument are 'unique' and 'identical'. When type='unique', each residual variances is a unique free parameter. When type='identical', all of the residual variances are given by a single free parameter. In this case, all the residual variances are constrained to be equal. However, no linear or non-liniear contraint function is used. Rather, a single parameter occurs in multiple locations by using the same label.
Depending on the value of the path argument, either an MxMatrix or and MxPath object that can be inspected, modified, and/or included in MxModel objects.
emxFactorModel, emxGrowthModel
# Create a residual variance matrix require(EasyMx) manVars <- paste0('x', 1:6) emxResiduals(manVars, lbound=1e-6) emxResiduals(manVars, type='identical') emxResiduals(manVars, path=TRUE)# Create a residual variance matrix require(EasyMx) manVars <- paste0('x', 1:6) emxResiduals(manVars, lbound=1e-6) emxResiduals(manVars, type='identical') emxResiduals(manVars, path=TRUE)
This function classifies time series (usually people) in a state space mixture model.
emxStateSpaceMixtureClassify(model)emxStateSpaceMixtureClassify(model)
model |
MxModel. The output from |
This is a helper function for state space mixture modeling. The function will almost exclusively be used in conjunction with emxStateSpaceMixtureModel. The present function takes a state space mixture model as input, and returns detailed information about the most likely class for each unique ID.
A named list with elements
estimated_classes |
A vector of the most likely class for each person. Dimension is people. |
joint_m2ll |
A matrix of joint minus two summed log likelihoods of each person and each class. Dimension is people by classes. |
m2ll |
A matrix of minus two summed log likelihoods of each person given each class. Dimension is people by classes. |
likelihood |
An matrix of the likelihoods (i.e., probability densities) of each combination of time point, person, and class. Dimension is people by classes where each element of the matrix is a single-element list of the likelihood of that person in that class across all times. |
emxStateSpaceMixtureModel , emxMixtureModel
# Example require(EasyMx) data(myFADataRaw) ds0 <- myFADataRaw[,1:3] # Make a VAR Model vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel') # Re-label parameters to have different AR parameters # for class 1 and class 2 vm1 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels, newlabels=paste0(vm$Dynamics$labels, '_k1'), name='klass1') vm2 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels, newlabels=paste0(vm$Dynamics$labels, '_k2'), name='klass2') # Pretend you have a data set of 50 people # each measured 10 times on 3 variables ds1 <- myFADataRaw[, 1:3] ds1$id <- rep(1:50, each=nrow(myFADataRaw)/50) ## Not run: # Make the state space mixture model ssmm <- emxStateSpaceMixtureModel(model=list(vm1, vm2), data=ds1, ID='id') # Fit model ssmmr <- mxRun(ssmm) # Extract estimated classes and diagnostics eclasses <- emxStateSpaceMixtureClassify(ssmmr) ## End(Not run)# Example require(EasyMx) data(myFADataRaw) ds0 <- myFADataRaw[,1:3] # Make a VAR Model vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel') # Re-label parameters to have different AR parameters # for class 1 and class 2 vm1 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels, newlabels=paste0(vm$Dynamics$labels, '_k1'), name='klass1') vm2 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels, newlabels=paste0(vm$Dynamics$labels, '_k2'), name='klass2') # Pretend you have a data set of 50 people # each measured 10 times on 3 variables ds1 <- myFADataRaw[, 1:3] ds1$id <- rep(1:50, each=nrow(myFADataRaw)/50) ## Not run: # Make the state space mixture model ssmm <- emxStateSpaceMixtureModel(model=list(vm1, vm2), data=ds1, ID='id') # Fit model ssmmr <- mxRun(ssmm) # Extract estimated classes and diagnostics eclasses <- emxStateSpaceMixtureClassify(ssmmr) ## End(Not run)
This function creates a state space mixture model as an MxModel object.
emxStateSpaceMixtureModel(model, data, name, run=FALSE, use, ID, time, ...) emxModelStateSpaceMixture(model, data, name, run=FALSE, use, ID, time, ...)emxStateSpaceMixtureModel(model, data, name, run=FALSE, use, ID, time, ...) emxModelStateSpaceMixture(model, data, name, run=FALSE, use, ID, time, ...)
model |
list of MxModel objects, each of which should be a state space model |
data |
data used for the model |
name |
character. Optional name of the model created. |
run |
logical. Whether to run the model before returning. |
use |
character vector. The names of the variables to use. Currently ignored. |
ID |
character. Name of variable that identifies each unique person. |
time |
character. Name of the variable that gives the time of each obsevation. Currently ignored. |
... |
Force later arguments to be named. ... is ignored. |
The idea of state space mixture modeling is to model multiple, multivariate time series while allowing for qualitative differences between the time series. Suppose you have a multivariate time series for several people. You think some people should have the same time series model, but not everyone. You think there should be a small number of homogeneous sets of people that follow the same time series model, but you do not know which people or the exact parameter values of the candidate time series models. This function presents one solution to this problem.
State space mixture modeling begins with a set of candidate state space models, and uses these state space models as the mixture classes. The goal is to simultaneously estimate the free parameters of the state space models, and estimate which multivariate time series (e.g., perseon) belongs to which mixture class.
The component state space models may share some free parameters or none. Note that free parameters with the same name are constrained to be equal across all models. Conversely, unnamed free parameters are given unique names and are allowed to differ for each person-mixture combination, which creates a very large number of free parameters. We strongly encourage you to name all of your free parameters in the model list to avoid melting your computer's CPU.
The model argument currently must be a list. The elements of the list should be MxModel objects. Each list element forms a mixture class in the final model.
This function creates a multigroup mixture model where the mixture classes are the elements of the model list argument. Each unique ID forms a group.
The data argument can be a list of data.frame objects with one element for each ID, or a single data.frame with an ID variable that separates groups.
An MxModel.
emxStateSpaceMixtureClassify , emxMixtureModel
# Example require(EasyMx) data(myFADataRaw) ds0 <- myFADataRaw[,1:3] # Make a VAR Model vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel') # Re-label parameters to have different AR parameters # for class 1 and class 2 vm1 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels, newlabels=paste0(vm$Dynamics$labels, '_k1'), name='klass1') vm2 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels, newlabels=paste0(vm$Dynamics$labels, '_k2'), name='klass2') # Pretend you have a data set of 50 people # each measured 10 times on 3 variables ds1 <- myFADataRaw[, 1:3] ds1$id <- rep(1:50, each=nrow(myFADataRaw)/50) ## Not run: # Make and fit the state space mixture model ssmm <- emxStateSpaceMixtureModel(model=list(vm1, vm2), data=ds1, ID='id', run=TRUE) ## End(Not run)# Example require(EasyMx) data(myFADataRaw) ds0 <- myFADataRaw[,1:3] # Make a VAR Model vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel') # Re-label parameters to have different AR parameters # for class 1 and class 2 vm1 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels, newlabels=paste0(vm$Dynamics$labels, '_k1'), name='klass1') vm2 <- OpenMx::omxSetParameters(vm, labels=vm$Dynamics$labels, newlabels=paste0(vm$Dynamics$labels, '_k2'), name='klass2') # Pretend you have a data set of 50 people # each measured 10 times on 3 variables ds1 <- myFADataRaw[, 1:3] ds1$id <- rep(1:50, each=nrow(myFADataRaw)/50) ## Not run: # Make and fit the state space mixture model ssmm <- emxStateSpaceMixtureModel(model=list(vm1, vm2), data=ds1, ID='id', run=TRUE) ## End(Not run)
This function creates a threshold matrix as an MxMatrix object.
emxThresholds(data, ordinalCols, deviation=TRUE)emxThresholds(data, ordinalCols, deviation=TRUE)
data |
The data frame or matrix for which thresholds should be created. |
ordinalCols |
optional character vector. The names of the ordinal variables in the data. |
deviation |
logical. Return the list of OpenMx objects needed for the deviation form of the threholds (default) or just the raw thresholds matrix |
An MxMatrix giving the thresholds.
emxFactorModel, emxGrowthModel
# Example require(EasyMx) data(jointdata) jointdata[, c(2, 4, 5)] <- mxFactor(jointdata[,c(2, 4, 5)], levels=sapply(jointdata[,c(2, 4, 5)], function(x){sort(unique(x))})) emxThresholds(jointdata, c(FALSE, TRUE, FALSE, TRUE, TRUE))# Example require(EasyMx) data(jointdata) jointdata[, c(2, 4, 5)] <- mxFactor(jointdata[,c(2, 4, 5)], levels=sapply(jointdata[,c(2, 4, 5)], function(x){sort(unique(x))})) emxThresholds(jointdata, c(FALSE, TRUE, FALSE, TRUE, TRUE))
This function creates an MxModel and associated objects for a basic Twin model.
emxTwinModel(model, relatedness, data, run=FALSE, use, name='model', components='ACE') emxModelTwin(model, relatedness, data, run=FALSE, use, name='model', components='ACE')emxTwinModel(model, relatedness, data, run=FALSE, use, name='model', components='ACE') emxModelTwin(model, relatedness, data, run=FALSE, use, name='model', components='ACE')
model |
Description of the model. Currently ignored. |
relatedness |
Description of the relatedness patterns. Currently the name of the variable that gives the coefficient of relatedness. |
data |
data.frame or matrix. The data set used in the model. |
run |
logical. Whether to run the model before returning. |
use |
character vector. Names of the variables used in the model. |
name |
character. Name of the model. |
components |
character. Name of the variance components to include. Current valid options are 'ACE' and 'ADE' |
Because the model argument is ignored and the relatedness argument has limited use, this function only constructs a very basic and rigid Twin model. It creates a Cholesky model with A, C, and E components or a Cholesky model with A, D, and E components. The means are constrained equal across twins.
MxModel.
# Create an ACE model in 10 lines # 8 of those are data handling. # 2 are the actual model. require(EasyMx) require(OpenMx) data(twinData) twinVar = names(twinData) selVars <- c('ht1', 'bmi1','ht2','bmi2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] ## Not run: run3 <- emxTwinModel(model='Cholesky', relatedness='RCoef', data=mzdzData, use=selVars, run=TRUE, name='TwCh') ## End(Not run)# Create an ACE model in 10 lines # 8 of those are data handling. # 2 are the actual model. require(EasyMx) require(OpenMx) data(twinData) twinVar = names(twinData) selVars <- c('ht1', 'bmi1','ht2','bmi2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] ## Not run: run3 <- emxTwinModel(model='Cholesky', relatedness='RCoef', data=mzdzData, use=selVars, run=TRUE, name='TwCh') ## End(Not run)
This function creates a variance components model as an MxModel object.
emxVarianceComponents(model, data, run)emxVarianceComponents(model, data, run)
model |
MxModel. See Details. |
data |
matrix or data.frame. The used in the model |
run |
logical. Whether to run the model before returning. |
This function does not really do anything currently. Do not use it.
MxModel.
# Create a loadings matrix require(EasyMx)# Create a loadings matrix require(EasyMx)
This function creates a vector autoregressive (VAR) model as an MxModel object.
emxVARModel(model, data, name, run=FALSE, use, ID) emxModelVAR(model, data, name, run=FALSE, use, ID)emxVARModel(model, data, name, run=FALSE, use, ID) emxModelVAR(model, data, name, run=FALSE, use, ID)
model |
Currently ignored, but later will specify particular kinds of VAR models |
data |
data used for the model |
name |
character. Optional name of the model created. |
run |
logical. Whether to run the model before returning. |
use |
character vector. The names of the variables to use. Currently ignored. |
ID |
character. Name of variable that identifies each unique person. |
The purpose of this function is to quickly specify a vector autoregressive model. It is currently in the early stages of development and might change considerable with regard to the model argument and the ID argument.
An MxModel.
emxStateSpaceMixtureModel , emxFactorModel
# Example require(EasyMx) data(myFADataRaw) ds0 <- myFADataRaw[,1:3] # Make a VAR Model vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel')# Example require(EasyMx) data(myFADataRaw) ds0 <- myFADataRaw[,1:3] # Make a VAR Model vm <- emxVARModel(data=ds0, use=names(ds0), name='varmodel')