### Mixture Modeling - FHSS Research Support Center

```Mixture Modeling
Chongming Yang
Research Support Center
FHSS College
Mixture of Distributions
Mixture of Distributions
Classification Techniques
• Latent Class Analysis (categorical indicators)
• Latent Profile Analysis (continuous Indicators)
• Finite Mixture Modeling (multivariate normal
variables)
• …
Integrate Classification Models into
Other Models
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Mixture Factor Analysis
Mixture Regressions
Mixture Structural Equation Modeling
Growth Mixture Modeling
Multilevel Mixture Modeling
Disadvantages of Multi-steps Practice
• Multistep practice
– Run classification model
– Save membership Variable
– Model membership variable and other variables
– Biases in parameter estimates
– Biases in standard errors
• Significance
• Confidence Intervals
Latent Class Analysis (LCA)
• Setting
– Latent trait assumed to be categorical
– Trait measured with multiple categorical indicators
– Example: drug addiction, Schizophrenia
• Aim
– Identify heterogeneous classes/groups
– Estimate class probabilities
– Identify good indicators of classes
– Relate covariates to Classes
Graphic LCA Model
• Categorical Indicators u: u1, u2,u3, …ur
• Categorical Latent Variable C: C =1, 2, …, or K
Probabilistic Model
• Assumption: Conditional independence of u
so that interdependence is explained by C like factor analysis model
• An item probability

( = 1) =
( = )( = 1| =
=1
• Joint Probability of all indicators
P(u1 , u2 , u3 ...ur ) 
k
 P(c  k ) P(u
k 1
1
| c  k ) P(u2 | c  k )...P(ur | c  k )
LCA Parameters
• Number of Classes -1
• Item Probabilities -1
Class Means (Logit)
• Probability Scale
1
( = 1|) =
1 + exp(−)
(logistic Regression without any Covariates x)
• Logit Scale
= [ ( 1 − )
• Mean (highest number of Class) = 0
Latent Class Analysis with Covariates
• Covariates  are related to Class Probability
with multinomial logistic regression
P(cik  1| xi ) 
 ck  ck x
e
K
e
J 1
 cj  cj x
Posterior Probability
(membership/classification of cases)
P(c  k )P(u1 | c  k )P(u2 | c  k )...P(ur | c  k )
P(c  k | u1, u2 ,...ur ) 
P(u1, u2 ,...ur )
Estimation
• Maximum Likelihood estimation via
• Expectation-Maximization algorithm
– E (expectation) step: compute average posterior
probabilities for each class and item
– M (maximization) step: estimate class and item
parameters
– Iterate EM to maximize the likelihood of the
parameters
Test against Data
• O = observed number of response patterns
• E = model estimated number of response
patterns
2
( o  e)
2
• Pearson
 
e
• Chi-square based on likelihood ratio
 2 LR  2 o log(o / e)
Determine Number of Classes
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Substantive theory (parsimonious, interpretable)
Predictive validity
Auxiliary variables / covariates
Statistical information and tests
– Bayesian Information Criterion (BIC)
– Entropy
– Testing K against K-1 Classes
• Vuong-Lo-Mendell-Rubin likelihood-ratio test
• Bootstrapped likelihood ratio test
Bayesian Information Criterion
(BIC)
BIC 2log(L)  (h)ln(N)
L = likelihood
h = number of parameters
N = sample size
Choose model with smallest BIC
BIC Difference > 4 appreciable
Quality of Classification
• Entropy
(− ln )
= 1 −
ln
–  = average of highest class probability of
individuals
– A value of close to 1 indicates good classification
– No clear cutting point for acceptance or rejection
Testing K against K-1 Classes
• Bootstrapped likelihood ratio test
LRT = 2[logL(model 1)- logL(model2)], where
model 2 is nested in model 1.
Bootstrap Steps:
1. Estimate LRT for both models
2. Use bootstrapped samples to obtain
distributions for LRT of both models
3. Compare LRT and get p values
Testing K against K-1 Classes
• Vuong-Lo-Mendell-Rubin likelihood-ratio test
Determine Quality of Indicators
• Good indicators
– Item response probability is close to 0 or 1 in each
class
– Item response probability is high in more than one
– Item response probability is low in all classes like
LCA Examples
• LCA
• LCA with covariates
• Class predicts a categorical outcome
Save Membership Variable
Variable:
idvar = id;
Output:
Savedata: File = cmmber.txt;
Save = cprob;
Latent Profile Analysis
• Covariance of continuous variables are
dependent on class K and fixed at zero
• Variances of continuous variables are constrained
to be equal across classes and minimized
• Mean differences are maximized across classes
11
0
•  =  =
0
0
22
0
0
0

Finite Mixture Modeling
(multivariate normal variables)
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Finite = finite number of subgroups/classes
Variables are normally distributed in each class
Means differ across classes
Variances are the same across
Covariances can differ without restrictions or
equal with restrictions across classes
• Latent profile can be special case with
covariances fixed at zero.
Mixture Factor Analysis
• Allow one to examine measurement
properties of items in heterogeneous
subgroups / classes
• Measurement invariance is not required
assuming heterogeneity
• Factor structure can change
• See Mplus outputs
Factor Mixture Analysis
• Parental Control
Parents let you make your own decisions about the time you must be home on weekend nights
Parents let you make your own decisions about the people you hang around with
Parents let you make your own decisions about what you wear
Parents let you make your own decisions about which television programs you watch
Parents let you make your own decisions about which television programs you watch
Parents let you make your own decisions about what time you go to bed on week nights
Parents let you make your own decisions about what you eat
• Parental Acceptance
Feel people in your family understand you
Feel you want to leave home
Feel you and your family have fun together
Feel that your family pay attention to you
Feel close to your mother
Feel close to your father
Two dimensions of Parenting
Mixture SEM
• See mixture growth modeling
Mixture Modeling with Known Classes
• Identify hidden classes within known groups
• Under nonrandomized experiments
– Impose equality constraints on covariates to
identify similar classes from known groups
– Compare classes that differ in covariates
```