# # Copyright 2007-2021 by the individuals mentioned in the source code history # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #------------------------------------------------------------------------------ # Author: Michael D. Hunter # Date: 2024.03.39 # Filename: test-ModelIdentification2.R # Purpose: Create further and more intense checks of the model identification # checking function. #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # Load OpenMx library(OpenMx) library(testthat) context("Model Identification 2") #------------------------------------------------------------------------------ # Outline of Tests # X 1 RAM Factor Model # Works # X 2 Latent growth curve model # Works # X 3 Latent growth curve model with definition variable loadings # Works but # TODO Check dimensions of Jacobian to verify that mxCheckID only used single values? # X 4 Single group ACE model using definition variable for relatedness # Works # X 5 Factor model with factor loading set to b0 + b1*data.sex # Works in theory but fails because defvar sorting # What if first and last rows have the same defvar values? # X 6 Multiple group (normal) model with definition variable on means # Works # X 7 Factor model that uses mxConstraint for fixing factor variance # Works but # Requires data to do a data-independent identification check # X 8 Multiple group (normal) model that uses mxConstraint to set # variance to b0 + b1*sex # Fails. As designed? #------------------------------------------------------------------------------ # TODO Add checks for models with definition variables but are not identified # Factor model with b0+b1*data.sex loading but data has only one value of sex. # This model is not identified. The b1 parameter should show as non-identified. #------------------------------------------------------------------------------ # TODO Add empirical identification functionality # TODO Add empirical identification checks #------------------------------------------------------------------------------ # Check 1 RAM Factor Model mv <- letters[23:26] c1 <- mxModel('RAM Factor Model', type='RAM', manifestVars=mv, latentVars='H', mxPath('H', mv, values=c(.2, .3, .4, .5), labels=paste0('load', 1:4)), mxPath('H', arrows=2, values=1, free=FALSE, labels='hvar'), mxPath(mv, arrows=2, values=c(.8, .9, 1.1, 1.2)), mxPath('one', mv, values=2:5)) c1id <- mxCheckIdentification(c1) omxCheckTrue(c1id$status) #------------------------------------------------------------------------------ # Check 2 Latent growth curve model data(myLongitudinalData) mld <- myLongitudinalData mv <- names(mld) lv <- c('Intercept', 'Slope') mld[, mv] <- matrix(1:5, nrow=nrow(mld), ncol=5, byrow=TRUE) c2 <- mxModel('Latent growth curve model', type='LISREL', manifestVars=list(ex=mv), latentVars=list(ex=lv), mxData(mld, 'raw'), mxPath(lv, arrows=2, connect='unique.pairs', values=c(1, .5, 1)), mxPath(lv[1], mv, values=1, free=FALSE), mxPath(lv[2], mv, values=-2:2, free=FALSE), mxPath(mv, arrows=2, values=.1, labels='evar'), mxPath('one', lv)) # c2r <- mxRun(c2) # summary(c2r) c2id <- mxCheckIdentification(c2) omxCheckTrue(c2id$status) #------------------------------------------------------------------------------ # Check 3 Latent growth curve model with definition variable loadings data(myLongitudinalData) mld <- myLongitudinalData mv <- names(mld) dv <- paste0('t', 1:5) lv <- c('L0', 'L1') mld[, dv] <- matrix(1:5, nrow=nrow(mld), ncol=5, byrow=TRUE) c3 <- mxModel('defvar LGM', type='RAM', manifestVars=mv, latentVars=lv, mxData(mld, 'raw'), mxPath(lv, arrows=2, connect='unique.pairs', values=c(1, .5, 1)), mxPath(lv[1], mv, values=1, free=FALSE), mxPath(lv[2], mv, labels=paste0('data.', dv), free=FALSE), mxPath(mv, arrows=2, values=.1, labels='evar'), mxPath('one', lv)) # c3r <- mxRun(c3) # summary(c3r) c3id <- mxCheckIdentification(c3) omxCheckTrue(c3id$status) # Check dimensions of Jacobian to verify that mxCheckID only used single values omxCheckTrue(nrow(c3id$jacobian) == 20) #------------------------------------------------------------------------------ # Check 4 Single group ACE model using definition variable for relatedness data(twinData) twinVar <- names(twinData) selVars <- c('ht1', 'ht2') mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg')) mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg] lv <- c('A1', 'A2', 'C1', 'C2', 'E1', 'E2') c4 <- mxModel('defvar ACE', type='LISREL', manifestVars=list(ex=selVars), latentVars=list(ex=lv), mxPath(lv, arrows=2, values=1, free=FALSE), mxPath('one', selVars, labels='m'), mxPath(lv, selVars, values=.2, labels=rep(c('a', 'c', 'e'), each=2)), mxPath(lv[1], lv[2], arrows=2, labels='data.RCoef', free=FALSE), mxPath(lv[3], lv[4], arrows=2, values=1, free=FALSE), mxData(mzdzData, 'raw')) c4r <- mxRun(c4) summary(c4r) c4id <- mxCheckIdentification(c4r) omxCheckTrue(c4id$status) #------------------------------------------------------------------------------ # Check 5 Factor model with factor loading set to b0 + b1*data.sex data(HS.ability.data) mv <- c('visual', 'cubes', 'flags', 'straight') selv <- c('id', 'Gender', 'agey', mv) c5d <- HS.ability.data[,selv] c5d$sex <- as.numeric(c5d$Gender) - 1 loadLab <- paste0('loads[', 1:4, ',1]') c5 <- mxModel(type='RAM', manifestVars=mv, latentVars='V', mxPath('V', mv, values=c(.2, .3, .4, .5), labels=loadLab, free=FALSE), mxPath('V', arrows=2, values=1, free=FALSE, labels='hvar'), mxPath(mv, arrows=2, values=c(.8, .9, 1.1, 1.2)), mxPath('one', mv, values=2:5), mxMatrix('Full', 4, 2, values=c(.2, -.9), free=TRUE, name='B', byrow=TRUE), mxMatrix('Full', 2, 1, values=c(1, NA), labels=c(NA, 'data.sex'), name='x'), mxAlgebra(B %*% x, 'loads'), mxData(c5d, 'raw') ) c5r <- mxRun(c5) summary(c5r) # What if first and last rows have the same defvar values? c5id <- mxCheckIdentification(c5r) omxCheckTrue(c5id$status) #------------------------------------------------------------------------------ # Check 6 Multiple group (normal) model with definition variable on means data(example2) mzd <- example2[example2$Zygosity %in% 'MZ', ] dzd <- example2[example2$Zygosity %in% 'DZ', ] #--------------------------------------- # Each group with its own data c6mz <- mxModel('MZ', mxData(mzd, 'raw'), mxMatrix('Full', 1, 2, values=c(1, -1), free=TRUE, labels=c('b0m', 'b1m'), name='BMZ'), mxMatrix('Full', 1, 1, values=.5, free=TRUE, labels='var', name='V'), mxAlgebra(b0m + b1m*(data.TwinNum - 1), name='M'), mxExpectationNormal(covariance='V', means='M', dimnames='X'), mxFitFunctionML()) c6dz <- mxModel('DZ', mxData(dzd, 'raw'), mxMatrix('Full', 1, 2, values=c(1, -1), free=TRUE, labels=c('b0m', 'b1m'), name='BDZ'), mxMatrix('Full', 1, 1, values=.5, free=TRUE, labels='var', name='V'), mxAlgebra(b0m + b1m*(data.TwinNum - 1), name='M'), mxExpectationNormal(covariance='V', means='M', dimnames='X'), mxFitFunctionML()) c6 <- mxModel('mg', c6mz, c6dz, mxFitFunctionMultigroup(c('MZ', 'DZ'))) c6r <- mxRun(c6) summary(c6r) omxCheckTrue(imxHasDefinitionVariable(c6)) omxManifestModelByParameterJacobian(c6, defvar.row=c(MZ=14, DZ=201)) omxManifestModelByParameterJacobian(c6, defvar.row=c(MZ=14, DZ=199)) c6id <- mxCheckIdentification(c6) omxCheckTrue(c6id$status) # Evaluating the definition variable at multiple values # when there are multiple groups too #--------------------------------------- # Inherited data from super-model #------------------------------------------------------------------------------ # Check 7 Factor model that uses mxConstraint for fixing factor variance mv <- letters[23:26] c7 <- mxModel('Constraint RAM Factor Model', type='RAM', manifestVars=mv, latentVars='H', mxPath('H', mv, values=c(.2, .3, .4, .5), labels=paste0('load', 1:4)), mxPath('H', arrows=2, values=1, labels='hvar'), mxPath(mv, arrows=2, values=c(.8, .9, 1.1, 1.2)), mxPath('one', mv, values=2:5), mxData(data.frame(w=NA, x=NA, y=NA, z=NA), 'raw'), mxConstraint(hvar == 1)) c7id <- mxCheckIdentification(c7) # Error, says it needs a data set # Checking ID for models with constraints now requires a data set # Desired behavior? omxCheckTrue(c7id$status) #------------------------------------------------------------------------------ # Check 8 Multiple group (normal) model that uses mxConstraint to set # variance to b0 + b1*sex data(example2) mzd <- example2[example2$Zygosity %in% 'MZ', ] dzd <- example2[example2$Zygosity %in% 'DZ', ] c8mz <- mxModel('MZ', mxData(mzd, 'raw'), mxMatrix('Full', 1, 2, values=c(1, -1), free=TRUE, labels=c('b0m', 'b1m'), name='BMZ'), mxMatrix('Full', 1, 1, values=.5, free=TRUE, labels='var', name='V'), mxAlgebra(b0m + b1m*(data.TwinNum - 1), name='M'), mxMatrix('Full', 1, 1, free=TRUE, labels='mf', name='MF'), mxConstraint(M == MF), mxExpectationNormal(covariance='V', means='MF', dimnames='X'), mxFitFunctionML()) c8dz <- mxModel('DZ', mxData(dzd, 'raw'), mxMatrix('Full', 1, 2, values=c(1, -1), free=TRUE, labels=c('b0m', 'b1m'), name='BDZ'), mxMatrix('Full', 1, 1, values=.5, free=TRUE, labels='var', name='V'), mxAlgebra(b0m + b1m*(data.TwinNum - 1), name='M'), mxMatrix('Full', 1, 1, free=TRUE, labels='df', name='DF'), mxConstraint(M == DF), mxExpectationNormal(covariance='V', means='M', dimnames='X'), mxFitFunctionML()) c8 <- mxModel('mg', c8mz, c8dz, mxFitFunctionMultigroup(c('MZ', 'DZ'))) c8r <- mxRun(c8) summary(c8r) # Hmm ... it looks like constraints with definition variables might not be honored #c8id <- mxCheckIdentification(c8) # Error: foreign function call #omxCheckTrue(c8id$status) #------------------------------------------------------------------------------ # End