Cross-Sectional Mixture Modeling
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Transcript of Cross-Sectional Mixture Modeling
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CROSS-SECTIONAL MIXTURE MODELINGShaunna L. ClarkAdvanced Genetic Epidemiology Statistical WorkshopOctober 23, 2012
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OUTLINE What is a mixture? Introduction to LCA (LPA)
Basic Analysis Ideas\Plan and Issues How to choose the number of classes
How do we implement mixtures in OpenMx? Factor Mixture Model What do classes mean for twin modeling?
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HOMOGENEITY VS. HETEROGENEITY Most models assume homogeneity
i.e. Individuals in a sample all follow the same model
What have seen so far (for the most part) But not always the case
Ex: Sex, Age, Patterns of Substance Abuse
Alcohol Tobacco Cannabis Opiates Heroin0
0.10.20.30.40.50.60.70.80.9
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WHAT IS MIXTURE MODELING
Used to model unobserved heterogeneity by identifying different subgroups of individuals
Ex: IQ, Religiosity
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LATENT CLASS ANALYSIS (LCA)Also known as Latent Profile Analysis (LPA) if you have continuously distributed variables
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LATENT CLASS ANALYSISIntroduced by Lazarsfeld & Henry, Goodman, Clogg, Dayton
& Mcready Setting
Cross-sectional data Multiple items measuring a construct
12 items measuring the construct of Cannabis Abuse/Dependence Hypothesized construct represented as latent class
variable (categorical latent variable) Different categories of Cannabis Abuse\Dependence patterns
Aim Identify items that indicate classes well Estimate proportion of sample in each class (class
probability) Classify individuals into classes (posterior probabilities)
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LATENT CLASS ANALYSIS CONT’D
x1 x2 x3 x4 x5
C
Alcohol Tobacco Cannabis Opiates Heroin0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
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Non UsersLegal Drug UsersMultiple Illicit Users
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LATENT CLASS ANALYSIS MODELDichotomous (0/1) indicators u: u1, u2, ... , ur
Categorical latent variable c: c = k ; k = 1, 2, ... , K
Marginal probability for item uj = 1,
(probability item uj =1 is the sum over all class of the product of the probability of being in class k and the probability of endorsing item uj given that you are in class k)
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JOINT PROBABILITIES Joint probability of all u’s, assuming conditional
independence:
Probability of observing a given response pattern is equal to the sum over all classes of the product of being in a given class and the probability of observing a response on item 1 given that you are in latent class k, . . . (repeat for each item)
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POSTERIOR PROBABILITIES
Probability of being in class k given your response pattern
Used to assign most likely class membership Based on highest posterior probability
Individual P(Class1) P(Class2) MLCMA .90 .10 1B .8 .2 2
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MODEL TESTING Log-likelihood ratio χ2 test (LLRT)
Overall test against the data with H1 being the unrestricted multinomial
Problem: Not distributed as χ2 due to boundary conditions Don’t use it!!! (McLachlan & Peele, 2000)
Information Criteria Akaike Information Criteria, AIC (Akaike,1974)
AIC = 2h-2ln(L) Bayesian Information Criteria, BIC (Schwartz, 1978)
BIC = -2ln(L)+h*ln(n) Where L = log-likelihood, h = number of parameters,
n = sample size Chose model with lowest value of IC
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OTHER TESTS Since can’t do LLRT, use test which
approximate the difference in LL values between k and k-1 class models. Vuong-Lo-Mendell-Rubin, LMR-LRT (Lo, Mendell, &
Rubin, 2001) Parametric bootstrapped LRT, BLRT (McLachlan,
1987) P-value is probability that H0 is true
H0: k-1 classes; H1: k classes A low p-value indicates a preference for the
estimated model (i.e. k classes) Look for the first time the p-value is non-
significant or greater than 0.05
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ANALYSIS PLAN1. Fit model with 1-class
Everyone in same class Sometimes simple is better
2. Fit LCA models 2-K classes3. Chose best number of classes
Seems simple right???
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NOT REALLY . . .LOTS OF KNOWN ISSUES IN MIXTURE ANALYSIS Global vs. Local Maximum
Log Likelihood
Parameter
GlobalLocal
Log Likelihood
GlobalLocal
Use multiple sets of random starting values to make sure have global solution. Make sure that best LL value has replicated
Parameter
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DETERMINING THE NUMBER OF CLASSES: CLASS ENUMERATION No agreed upon way to determine the correct
number of latent classes Statistical comparisons (i.e. ICs, LRTs) Interpretability and usefulness of classes
Substantive theory Relationship to auxiliary variables Predictive validity of classes Class size
Quality of Classifications (not my favorite) Classification table based on posterior probabilities Entropy - A value close to 1 indicates good
classification in that many individuals have posterior probabilities close to 0 or 1
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SUGGESTED STRATEGY Nylund et al. (2007), Tofighi & Enders (2008),
among others Simulation studies comparing tests and
information criteria described previously Suggest:
Use BIC and LMR to narrow down the number of plausible models
Then run BLRT on those models because BLRT can be computationally intensive
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MIXTURES IN OPENMX Specify class-specific models
Create MxModel objects for each class Specify class probabilities
Create an MxMatrix of class probabilities\proportions
Specify model-wide objective function Pull everything together in a parent model with
data Weighted sum of the class models
Estimate entire model
Note: One of potentially many ways to do this
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CLASS SPECIFIC MODELSnameList <- names(<dataset>)
class1 <- mxModel("Class1",
mxMatrix("Iden", name = "R", nrow = nvar, ncol = nvar, free=FALSE),
mxMatrix("Full", name = "M", nrow = 1, ncol = nvar, free=FALSE),
mxMatrix("Full", name = "ThresholdsClass1", nrow = 1, ncol = nvar, dimnames = list("Threshold",nameList), free=TRUE),
mxFIMLObjective(covariance="R", means="M", dimnames=nameList, thresholds="ThresholdsClass1",vector=TRUE))
Repeat for every class in your modelDon’t be like me, make sure to change class numbers
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DEFINE THE MODELlcamodel <- mxModel("lcamodel", class1, class2,
mxData(vars, type="raw"),
Next, specify class membership probabilities
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CLASS MEMBERSHIP PROBABILITIES When specifying need to remember:
1. Class probabilities must be positive2. Must sum to a constant - 1
mxMatrix("Full", name = "ClassMembershipProbabilities", nrow = nclass, ncol = 1, free=TRUE, labels = c(paste("pclass", 1:nclass, sep=""))),
mxBounds(c(paste("pclass", 1:nclass, sep="")),0,1),
mxMatrix("Iden", nrow = 1, name = "constraintLHS"),
mxAlgebra(sum(ClassMembershipProbabilities), name = "constraintRHS"),
mxConstraint(constraintLHS == constraintRHS),
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MODEL-WIDE OBJECTIVE FUNCTION Weighted sum of individual class likelihoods Weights are class probabilities
So for two classes:
€
−2LL = −2* log pkLki=1
k∑( )
€
−2LL = −2* log(p1L1 + p2L2)
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MODEL WIDE OBJECTIVE FUNCTION CONT’D mxAlgebra(
-2*sum(log(pclass1%x%Class1.objective + pclass2%x%Class2.objective)), name="lca"),
mxAlgebraObjective("lca"))
)
Now we run the model:model <- mxRun(lcamodel)
And we wait and wait and wait till it’s done.
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PROFILE PLOT One way to interpret the classes is to plot
them. In our example we had binary items, so the
thresholds are what distinguishes between classes Can plot the thresholds
Or you can plot the probabilities More intuitive Easier for non-statisticians to understand
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PROFILE PLOTS IN R\OPENMX#Pulling out thresholdsclass1T <-
model@output$matrices$Class1.ThresholdsClass1
class2T <- model@output$matrices$Class2.ThresholdsClass2
#Converting threshold to probabilitiesclass1P<-t(1/(1+exp(-class1T)))class2P<-t(1/(1+exp(-class2T)))
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PROFILE PLOTS CONT’Dplot(class1P, type="o", col="blue",ylim=c(0,1),axes=FALSE,
ann=FALSE)axis(1,at=1:12,lab=nameList)axis(2,las=1,at=c(0,0.2,0.4,0.6,0.8,1))box()lines(class2P,type="o", pch=22, lty=2, col="red")title(main="LCA 2 Class Profile Plot", col.main="black",font.main=4)title(xlab="DSM Items", col.lab="black")title(ylab="Probability", col.lab="black")legend("bottomright",c("Class 1","Class 2"), cex=0.8,
col=c("blue","red"),pch=21:22,lty=1:2)
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OPENMX EXERCISE Unfortunately, it takes long time for these to
run so not feasible to do in this session However, I’ve run the 2-, 3-, and 4- class LCA
models for this data and (hopefully) the .Rdata files are posted on the website
Exercise: Using the .Rdata files1. Determine which model is better according to AIC\BIC
Want the lowest value2. Make a profile plot of the best solution and interpret
the classes What kind of substances users are there?
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CODE TO PULL OUT LL AND COMPUTE AIC\BIC#Pull out LLLL_2c <- model@output$Minus2LogLikelihoodLL_2cnsam = 1878
#parametersnpar <- (nclass-1) + (nthresh*nvar*nclassnpar
#Compute AIC & BICAIC_2c = 2*npar + LL_2c AIC_2c
BIC_2c = LL_2c + (npar*log(nsam))BIC_2c
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TABLE OF RESULTS
# Classes -2*LL Npar AIC BIC2 6589.96 25 6639 67783 6329.95 38 6405 66164 6308.96 51 6410 6693
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PROBLEM WITH LCA Once in a class, everyone “looks” the same. In the context of substance abuse, unlikely
that every user will have the same patterns of use Withdrawal, tolerance, hazardous use There is variation within a latent class
Severity
One proposed solution is the factor mixture model Uses a latent class variables to classify
individuals and latent factor to model severity
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FACTOR MIXTURE MODEL σ2F
x1 x2 x3 x4 x5
C F
λ1 λ2 λ3 λ4 λ5
Classes can be indicated by item thresholds (categorical)\ item means (continuous) or factor mean and variance
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GENERAL FACTOR MIXTURE MODEL
yik = Λk ηik + εik ,
ηik = αk + ζik ,where,
ζik ~ N(0, Ψk)
Similar to the FA model, except many parameters can be class varying as indicated by the subscript k
Several variations of this model which differ in terms of the measurement invariance Lubke & Neale (2005), Clark et al. (2012)
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FMM PROFILE PLOT
Alcohol Tobacco Cannabis Opiates Heroin0
0.1
0.2
0.3
0.4
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Non UsersUsers
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HOW DO WE DO THIS IN OPENMX?
You’ll have to wait till tomorrow! Factor Mixture Model is a generalization of
the Growth Mixture Model we’ll talk about tomorrow afternoon.
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OPTION 1: ACE DIRECTLY ON THE CLASSES
x1A x2A x3A x4A
CA
aA
cA
eA
x1B x2B x3B x4B
CB
aBcB
eB
1.0
1.0 (MZ) / 0.5 (DZ)
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WHAT WOULD THIS LOOK LIKE FOR THE FMM?
x1A x2A x3A x4A
CA
aA
cA
eA
x1B x2B x3B x4B
CB
aB
cB
eB
1.0
1.0 (MZ) / 0.5 (DZ)
FA
eA
cA
aA
eA
cA
aA
1.0 (MZ) / 0.5 (DZ) 1.0
FB
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FMM & ACE CONT’D One of many possible ways to do FMM & ACE in the
same model Can also have class specific ACE on the factors
Each class has own heritability
From Muthén et al. (2006)
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ISSUE WITH OPTION 1 Model is utilizes the liability threshold model
to “covert” the latent categorical variable, C, to a latent normal variable This requires that classes are ordered
Ex: high, medium, low users Don’t always have nicely ordered classes
Models are VERY time intensive Take a vacation for a week or two
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OPTION 2: THREE-STEP METHOD1. Estimate mixture model2. Assign individuals into their most likely
latent class based on the posterior probabilities of class membership
3. Use the observed, categorical variable of assigned class membership as the phenotype in a liability threshold model version of ACE analysis
Note: Requires ordered classes
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OPTION 2A Contingency table analysis using most likely
class membership Concordance between twins in terms of most likely
class membership If your classes are not ordered
Odds Ratio Excess twin concordance due to stronger genetic relationship
can be represented by the OR for MZ twins compared to the OR for DZ twins.
Place restrictions on the contingency table to test specific hypotheses Mendelian segregation, only shared environmental
effects Eaves (1993)
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ISSUES WITH OPTION 2 Potential for biased parameter estimates and
underestimated standard errors Assigned membership ignores fractional class
membership suggested by posterior probabilities Treat the classification as not having any
sampling error Good option when entropy is high\ well
separated classes
Individual P(Class1) P(Class2) MLCMA .90 .10 1B .8 .2 2C .51 .49 1
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SELECTION OF CROSS-SECTIONAL MIXTURE GENETIC ANALYSIS WRITINGS Latent Class Analysis
Eaves, 1993; Muthén et al., 2006; Clark, 2010
Factor Mixture Analysis Neale & Gillespie, 2005 (?); Clark, 2010; Clark et al.
(in preparation)
Additional References McLachlan, Do, & Ambroise, 2004
Mixtures in Substance Abuse Gillespie (2011, 2012)
Great cannabis examples