Mixture models are measurement models that use observed variables as indicators of one or more nominal latent variables (i.e. categorical variables). One way to think about mixture models that one is attempting to identify subsets or "classes" of observations within the observed data. The latent variable (classes) is categorical, but the indicators may be either categorical or continuous. The term latent class analysis is often used to refer to a mixture model in which all of the observed indicator variables are categorical.

Mplus version 5.2 was used for these examples.

### 1.0 Latent class analysis

The examples on this page use a dataset with information on high school students’ academic histories. In the first example below, a 2 class model is estimated using four dichotomous variables as indicators (category 1 = no, category 2 = yes). The variables are whether the student had taken honors math (

hm), honors English (he), or vocational classes (voc); and whether the student reported they were unlikely to go to college (nocol). The expected classes are academically oriented students (i.e. students who took honors classes, did not take vocational classes and reported they were likely to go to college), and students who are less academically oriented. The dataset for this example is https://stats.idre.ucla.edu/wp-content/uploads/2016/02/lca.dat.The input file for this model is shown below. The

usevariablesoption of the of thevariables:command specifies which variables will be used in this analysis (necessary when not all of the variables in the dataset are used). Theclassesoption identifies the name of the latent variable (in this casec), followed by the number of classes to be estimated in parentheses (in this case 2). Note that the class variable(s) can be assigned any valid variable name. Thecategoricaloption of thevariables:command tells Mplus which variables are categorical. Thetypeoption of theanalysis:command specifies the type of model to be estimated, in this case a mixture model.

TITLE: A latent class analysis (LCA) Data: file is https://stats.idre.ucla.edu/wp-content/uploads/2016/02/lca.dat; Variable: names are hm he voc nocol ach9-ach12; usevariables are hm he voc nocol ; classes = c (2); categorical = hm he voc nocol ; Analysis: type = mixture;

The output for this model is shown below.

INPUT READING TERMINATED NORMALLY A latent class analysis (LCA) SUMMARY OF ANALYSIS Number of groups 1 Number of observations 500 Number of dependent variables 4 Number of independent variables 0 Number of continuous latent variables 0 Number of categorical latent variables 1 Observed dependent variables Binary and ordered categorical (ordinal) HM HE VOC NOCOL Categorical latent variables C Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 100 Convergence criterion 0.100D-05 Optimization Specifications for the EM Algorithm Maximum number of iterations 500 Convergence criteria Loglikelihood change 0.100D-06 Relative loglikelihood change 0.100D-06 Derivative 0.100D-05 Optimization Specifications for the M step of the EM Algorithm for Categorical Latent variables Number of M step iterations 1 M step convergence criterion 0.100D-05 Basis for M step termination ITERATION Optimization Specifications for the M step of the EM Algorithm for Censored, Binary or Ordered Categorical (Ordinal), Unordered Categorical (Nominal) and Count Outcomes Number of M step iterations 1 M step convergence criterion 0.100D-05 Basis for M step termination ITERATION Maximum value for logit thresholds 15 Minimum value for logit thresholds -15 Minimum expected cell size for chi-square 0.100D-01 Optimization algorithm EMA Random Starts Specifications Number of initial stage random starts 10 Number of final stage optimizations 2 Number of initial stage iterations 10 Initial stage convergence criterion 0.100D+01 Random starts scale 0.500D+01 Random seed for generating random starts 0 Link LOGIT Input data file(s) https://stats.idre.ucla.edu/wp-content/uploads/2016/02/lca.dat Input data format FREE SUMMARY OF CATEGORICAL DATA PROPORTIONS HM Category 1 0.678 Category 2 0.322 HE Category 1 0.686 Category 2 0.314 VOC Category 1 0.322 Category 2 0.678 NOCOL Category 1 0.334 Category 2 0.666 RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers: -965.244 93468 3 -965.244 939021 8 THE MODEL ESTIMATION TERMINATED NORMALLY TESTS OF MODEL FIT Loglikelihood H0 Value -965.244 H0 Scaling Correction Factor 1.013 for MLR Information Criteria Number of Free Parameters 9 Akaike (AIC) 1948.488 Bayesian (BIC) 1986.420 Sample-Size Adjusted BIC 1957.853 (n* = (n + 2) / 24) Chi-Square Test of Model Fit for the Binary and Ordered Categorical (Ordinal) Outcomes Pearson Chi-Square Value 6.287 Degrees of Freedom 6 P-Value 0.3918 Likelihood Ratio Chi-Square Value 5.605 Degrees of Freedom 6 P-Value 0.4688 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES BASED ON THE ESTIMATED MODEL Latent Classes1 136.38198 0.27276 2 363.61802 0.72724FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Classes 1 136.38170 0.27276 2 363.61830 0.72724 CLASSIFICATION QUALITY Entropy 0.904 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP Class Counts and Proportions Latent Classes1 127 0.25400 2 373 0.74600Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column) 1 2 1 0.986 0.014 2 0.030 0.970 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Latent Class 1 Thresholds HM$1 -2.063 0.373 -5.536 0.000 HE$1 -1.724 0.300 -5.755 0.000 VOC$1 2.331 0.389 5.985 0.000 NOCOL$1 2.078 0.320 6.490 0.000 Latent Class 2 Thresholds HM$1 2.091 0.182 11.502 0.000 HE$1 2.056 0.180 11.401 0.000 VOC$1 -2.187 0.203 -10.760 0.000 NOCOL$1 -1.937 0.183 -10.613 0.000 Categorical Latent Variables Means C#1 -0.981 0.116 -8.468 0.000 RESULTS IN PROBABILITY SCALE Latent Class 1HM Category 1 0.113 0.037 3.025 0.002 Category 2 0.887 0.037 23.799 0.000HE Category 1 0.151 0.038 3.934 0.000 Category 2 0.849 0.038 22.056 0.000 VOC Category 1 0.911 0.031 28.987 0.000 Category 2 0.089 0.031 2.817 0.005 NOCOL Category 1 0.889 0.032 28.072 0.000 Category 2 0.111 0.032 3.514 0.000 Latent Class 2 HM Category 1 0.890 0.018 50.016 0.000 Category 2 0.110 0.018 6.181 0.000 HE Category 1 0.887 0.018 48.873 0.000 Category 2 0.113 0.018 6.256 0.000 VOC Category 1 0.101 0.018 5.472 0.000 Category 2 0.899 0.018 48.748 0.000 NOCOL Category 1 0.126 0.020 6.267 0.000 Category 2 0.874 0.020 43.498 0.000 LATENT CLASS ODDS RATIO RESULTS Latent Class 1 Compared to Latent Class 2 HM Category > 1 63.670 25.875 2.461 0.014 HE Category > 1 43.795 14.941 2.931 0.003 VOC Category > 1 0.011 0.005 2.351 0.019 NOCOL Category > 1 0.018 0.007 2.768 0.006 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.600E-01 (ratio of smallest to largest eigenvalue)

Towards the top of the output, under FINAL CLASS COUNTS…, Mplus gives the final counts and proportions for the classes in several ways. First it gives the counts (i.e. the number of cases in each class) and proportions based on the estimated model, and on the posterior probabilities. This gives the proportion (and count) of individuals estimated to be in each class in the model. Below that, Mplus gives the classification based on most likely class membership, which is an alternative method of assigning individuals to classes. Based on the estimated model and posterior probabilities we see that about 27% of students belong to class 1, and about 73% belong to class 2. Based on most likely class membership, about 25% of students belong to class 1 and the remaining 75% to class 2. Under MODEL RESULTS the thresholds for the classes are listed. Thresholds are on the logit scale, and hence, can be somewhat difficult to interpret. The same information is given in a more interpretable scale under RESULTS IN PROBABILITY SCALE. Here we see that the probability that an individual in class 1 will be in category 2 of the variable

hmis .89. In other words, the estimated probability of a student in class 1 taking honors math is about .89.

### 2.0 Using both categorical and continuous indicator variables

Above we estimated a specific case of a mixture model, a latent class analysis, in which all of the indicators are categorical, in this example the model contains both categorical and continuous indicators. In addition to the four categorical variables used in the example above, this model includes four continuous variables, the students score on a measure of academic achievement for each of the four years of high school (

ach9–ach12). The achievement variables have been centered so that each has a mean of zero. The only difference between the input file for this model and the one for the LCA estimated above is that theusevariablesoption has been dropped because all variables in the dataset are used in the model. In general, the only difference between the input file for a mixture model with all categorical indicators and the input for a model that includes continuous variables is the type of variables included.

Title: Categorical and continuous indicators Data: file is https://stats.idre.ucla.edu/wp-content/uploads/2016/02/lca.dat; Variable: names are hm he voc nocol ach9-ach12; classes = c (2); categorical = hm he voc nocol ; Analysis: type = mixture;

Below is the output for this model.

*** WARNING in MODEL command All variables are uncorrelated with all other variables within class. Check that this is what is intended. 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONSCategorical and continuous indicators SUMMARY OF ANALYSIS Number of groups 1 Number of observations 500 Number of dependent variables 8 Number of independent variables 0 Number of continuous latent variables 0 Number of categorical latent variables 1 Observed dependent variables Continuous ACH9 ACH10 ACH11 ACH12 Binary and ordered categorical (ordinal) HM HE VOC NOCOL Categorical latent variables C Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 100 Convergence criterion 0.100D-05 Optimization Specifications for the EM Algorithm Maximum number of iterations 500 Convergence criteria Loglikelihood change 0.100D-06 Relative loglikelihood change 0.100D-06 Derivative 0.100D-05 Optimization Specifications for the M step of the EM Algorithm for Categorical Latent variables Number of M step iterations 1 M step convergence criterion 0.100D-05 Basis for M step termination ITERATION Optimization Specifications for the M step of the EM Algorithm for Censored, Binary or Ordered Categorical (Ordinal), Unordered Categorical (Nominal) and Count Outcomes Number of M step iterations 1 M step convergence criterion 0.100D-05 Basis for M step termination ITERATION Maximum value for logit thresholds 15 Minimum value for logit thresholds -15 Minimum expected cell size for chi-square 0.100D-01 Optimization algorithm EMA Random Starts Specifications Number of initial stage random starts 10 Number of final stage optimizations 2 Number of initial stage iterations 10 Initial stage convergence criterion 0.100D+01 Random starts scale 0.500D+01 Random seed for generating random starts 0 Link LOGIT Input data file(s) https://stats.idre.ucla.edu/wp-content/uploads/2016/02/lca.dat Input data format FREE SUMMARY OF CATEGORICAL DATA PROPORTIONS HM Category 1 0.678 Category 2 0.322 HE Category 1 0.686 Category 2 0.314 VOC Category 1 0.322 Category 2 0.678 NOCOL Category 1 0.334 Category 2 0.666 RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers: -3842.353 unperturbed 0 -3842.353 462953 7 THE MODEL ESTIMATION TERMINATED NORMALLY TESTS OF MODEL FIT Loglikelihood H0 Value -3842.353 H0 Scaling Correction Factor 0.982 for MLR Information Criteria Number of Free Parameters 21 Akaike (AIC) 7726.706 Bayesian (BIC) 7815.213 Sample-Size Adjusted BIC 7748.557 (n* = (n + 2) / 24) Chi-Square Test of Model Fit for the Binary and Ordered Categorical (Ordinal) Outcomes Pearson Chi-Square Value 7.628 Degrees of Freedom 6 P-Value 0.2666 Likelihood Ratio Chi-Square Value 6.974 Degrees of Freedom 6 P-Value 0.3233 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES BASED ON THE ESTIMATED MODEL Latent Classes 1 367.56581 0.73513 2 132.43419 0.26487 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Classes 1 367.56581 0.73513 2 132.43419 0.26487 CLASSIFICATION QUALITY Entropy 0.998 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP Class Counts and Proportions Latent Classes 1 368 0.73600 2 132 0.26400 Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column) 1 2 1 0.999 0.001 2 0.000 1.000 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Latent Class 1Means ACH9 -2.058 0.055 -37.121 0.000 ACH10 -2.061 0.051 -40.656 0.000 ACH11 -0.987 0.055 -18.070 0.000 ACH12 -0.990 0.052 -19.023 0.000Thresholds HM$1 2.021 0.162 12.453 0.000 HE$1 2.075 0.166 12.521 0.000 VOC$1 -2.075 0.166 -12.525 0.000 NOCOL$1 -1.931 0.157 -12.280 0.000Variances ACH9 1.116 0.073 15.346 0.000 ACH10 0.956 0.058 16.601 0.000 ACH11 1.031 0.059 17.382 0.000 ACH12 0.946 0.060 15.727 0.000Latent Class 2Means ACH9 1.988 0.091 21.870 0.000 ACH10 1.971 0.087 22.653 0.000 ACH11 0.987 0.081 12.248 0.000 ACH12 0.829 0.080 10.425 0.000Thresholds HM$1 -2.101 0.282 -7.440 0.000 HE$1 -1.954 0.266 -7.354 0.000 VOC$1 2.267 0.302 7.514 0.000 NOCOL$1 2.306 0.303 7.617 0.000Variances ACH9 1.116 0.073 15.346 0.000 ACH10 0.956 0.058 16.601 0.000 ACH11 1.031 0.059 17.382 0.000 ACH12 0.946 0.060 15.727 0.000Categorical Latent Variables Means C#1 1.021 0.102 10.055 0.000 RESULTS IN PROBABILITY SCALE Latent Class 1 HM Category 1 0.883 0.017 52.665 0.000 Category 2 0.117 0.017 6.977 0.000 HE Category 1 0.888 0.016 54.096 0.000 Category 2 0.112 0.016 6.792 0.000 VOC Category 1 0.112 0.016 6.794 0.000 Category 2 0.888 0.016 54.114 0.000 NOCOL Category 1 0.127 0.017 7.283 0.000 Category 2 0.873 0.017 50.207 0.000 Latent Class 2 HM Category 1 0.109 0.027 3.974 0.000 Category 2 0.891 0.027 32.487 0.000 HE Category 1 0.124 0.029 4.296 0.000 Category 2 0.876 0.029 30.323 0.000 VOC Category 1 0.906 0.026 35.304 0.000 Category 2 0.094 0.026 3.658 0.000 NOCOL Category 1 0.909 0.025 36.451 0.000 Category 2 0.091 0.025 3.632 0.000 LATENT CLASS ODDS RATIO RESULTS Latent Class 1 Compared to Latent Class 2 HM Category > 1 0.016 0.005 3.066 0.002 HE Category > 1 0.018 0.006 3.188 0.001 VOC Category > 1 76.870 26.448 2.906 0.004 NOCOL Category > 1 69.181 23.612 2.930 0.003 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.275E-01 (ratio of smallest to largest eigenvalue)

Towards the top of the output is a message warning us that all of the variables are uncorrelated within clusters. This “warning” does not imply a problem with the model, it is merely there to remind the user that the restriction exists, whether this restriction is appropriate must be determined by the user. In addition to the thresholds for the categorical items (which were included in the output for the previous example), the output for this model includes means and variances for the continuous indicators (i.e.

ach9–ach12). The means for the academic achievement variables (ach9–ach12) are all lower in the first class than the second class. The first class is also less likely to have taken honors classes (hmandhe) and more likely to have taken vocational classes (voc) and to say they don’t intend to go to college (nocol). Although the order of the classes has reversed (i.e. the class we have called "academically oriented students" is class 2 in this model) the results of this model are consistent with the results from the model in the first example. The models in both examples are consistent with hypothesis that there are two types of students, those who are academically oriented, and those who are not. Note that by default, Mplus specifies the model so that it assumes the variances of the continuous class indicators (ach9–ach12) are equal across all classes, this assumption may or may not be appropriate.

### 3.0 Saving Class Assignments

In addition to the output file produced by Mplus, it is possible to save class membership information for each case in the dataset to a text file. This text file can later be used with Mplus or read into another statistical package. To do this the

savedata:command is added to the input file. Thefileoption gives the name of the file in which the class assignments should be saved (i.e. class.txt). Whenever thefileoption is used, all of the variables used in the analysis are saved in an external file. Thesave = cprob;option specifies that the class probabilities should be saved, in addition to the variables used in estimation. Additional variables that were not used in the analysis, but which you wish to include in the saved file, for example, an id variable, can be included by adding theauxiliaryoption (e.g.auxiliary = id;) to thevariable:command.

Title: Saving class probabilities Data: file is https://stats.idre.ucla.edu/wp-content/uploads/2016/02/lca.dat; Variable: names are hm he voc nocol ach9-ach12; usevariables are hm he voc nocol ; classes = c (2); categorical = hm he voc nocol ; Analysis: type = mixture;Savedata: file is class.txt; save = cprob;

The output file for this model contains all of the information contained in the output for the model in the first example, plus additional output associated with the

savedata:command. This additional output appears towards the end of the output file, and is shown below.

SAVEDATA INFORMATION Order and format of variables HM F10.3 HE F10.3 VOC F10.3 NOCOL F10.3 CPROB1 F10.3 CPROB2 F10.3 C F10.3 Save file class.txt Save file format 7F10.3 Save file record length 5000

The additional output associated with the

savedata:command lists the variables in the order in which they appear in the saved dataset. Note that the 4 observed variables used in estimation are listed first, followed by three variables associated with the latent class assignment. The variablesCPROB1andCPROB2give the probability that each case is in class 1 or class 2, respectively. The variable C contains the class assignment based on posterior probabilities. Below the list of variables the name of the file, and information on the format of the file are shown.The file

class.txtis a text file that can be read by a large number of programs. The first few lines of this file are shown below. Based on the information in the output file, we know that the first four columns contain each student’s value for the variableshm,hw,voc, andnocol(in that order), the remaining three columns are each student’s predicted probability for each of the two classes, and the final column contains the student’s class membership.

1.000 1.000 0.000 1.000 0.963 0.037 1.000 1.000 0.000 0.000 0.000 0.971 0.029 1.000 0.000 0.000 1.000 1.000 0.000 1.000 2.000 1.000 1.000 0.000 0.000 0.999 0.001 1.000 1.000 1.000 0.000 0.000 0.999 0.001 1.000

### 4.0 Plots

Plots based on the estimated model can also be requested by adding the

plot:command to the input file. Thetypeoption specifies the type of plots desired, in this case,plot3requests all plots available for this model. Theseriesoption gives the variables to be included in the plots, this can contain either categorical or continuous variables (but not both at the same time). The list of variables in theseriesoption is followed by(*)this uses the defaults for the scaling of the x-axis in the plots. For more information on scaling of the x-axis see the Mplus manual.

Title: Categorical and continuous indicators Data: file is https://stats.idre.ucla.edu/wp-content/uploads/2016/02/lca.dat; Variable: names are hm he voc nocol ach9-ach12; classes = C (2); categorical = hm he voc nocol ; Analysis: type = mixture;Plot: type = plot3; series = ach9-ach12(*);

From the **Graph** menu select **View graphs**. Because the variables we wish to plot are continuous,
we select **Estimated means**, for categorical variables we would select **
Estimated probabilities**. The
options under **View graphs** are somewhat limited for this model, if you
were to specify a model where class membership was predicted by additional variables, then a larger variety of graphs
is available.

This graph, sometimes called a profile plot, shows graphically the latent class means given in the MODEL RESULTS section of the output for the second example. By default, the x-axis starts at zero and increases in units of one for each of the observed variables. In our example, this means that the means for the variable ach9 shown at 0, followed by ach10 at 1, etc.

The legend tells us that class 1 is shown in red, and class 2 in green. It
also gives the proportion of cases in each class, in this case an estimated 26%
of students are in class 1, and 74% are in class 2. This information can be found in the output
under the heading "Final Class Counts and Proportions for the latent Classes Based
on the Estimated Model". Consistent with the means shown in the output for
example 2,the plot shows that students in class 1 have lower average scores on all four of the achievement variables
(**ach9**–**ach12**) than students in class 2.