A SUMMARY OF THE Mplus LANGUAGE

 

 

This chapter contains a summary of the commands, options, and settings of the Mplus language. For each command, default settings are found in the last column. Commands and options can be shortened to four or more letters. Option settings can be referred to by either the complete word or the part of the word shown in bold type.

 

THE TITLE COMMAND

 

 

TITLE:

title for the analysis

 

 

THE DATA COMMAND

 

 

DATA:

 

 

 

 

 

FILE IS

file name;

 

FORMAT IS

format statement;

 

 

FREE;

FREE;

TYPE IS

INDIVIDUAL;

INDIVIDUAL;

 

COVARIANCE;

 

 

CORRELATION;

 

 

FULLCOV;

 

 

FULLCORR;

 

 

MEANS;

 

 

STDEVIATIONS;

 

 

MONTECARLO;

 

 

IMPUTATION;

 

NOBSERVATIONS ARE

number of observations;

 

NGROUPS =

number of groups;

1

VARIANCES =

CHECK;

NOCHECK;

CHECK;

 

 


THE VARIABLE COMMAND

 

 

VARIABLE:

 

 

 

 

 

NAMES ARE

names of variables in the data set;

 

USEOBSERVATIONS ARE

conditional statement to select observations;

all observations in data set

USEVARIABLES ARE

names of analysis variables;

all variables in NAMES

MISSING ARE

variable (#);

 

 

. ;

 

* ;

 

 

BLANK;

 

CENSORED ARE

names, censoring type, and inflation status for censored dependent variables;

 

CATEGORICAL ARE

names of binary and ordered categorical (ordinal) dependent variables;

 

NOMINAL ARE

names of unordered categorical (nominal) dependent variables;

 

COUNT ARE

names and inflation status for count variables;

 

GROUPING IS

name of grouping variable (labels);

 

IDVARIABLE IS

name of ID variable;

 

WEIGHT IS

name of weight variable;

name of weight variable (SAMPLING);

name of weight variable (FREQUENCY);

SAMPLING

CLUSTER IS

name of cluster variable;

 

STRATIFICATION IS

name of stratification variable;

 

CENTERING IS

GRANDMEAN (variable names);

GROUPMEAN (variable names);

 

TSCORES ARE

names of observed variables with information on individually-varying times of observation;

 

AUXILIARY =

names of auxiliary variables;

 

 

 

 

CLASSES =

names of categorical latent variables (number of latent classes);

 


 

KNOWNCLASS =

name of categorical latent variable with known class membership (labels);

 

TRAINING =

names of training variables;

names of variables (MEMBERSHIP);

names of variables (PROBABILITIES);

MEMBERSHIP

WITHIN ARE

names of individual-level observed variables;

 

BETWEEN ARE

names of cluster-level observed variables;

 

PATTERN IS

name of pattern variable (patterns);

 

COHORT IS

name of cohort variable (values);

 

COPATTERN IS

name of cohort/pattern variable (patterns);

 

COHRECODE =

(old value = new value);

 

TIMEMEASURES =

list of sets of variables separated by the | symbol;

 

TNAMES =

list of root names for the sets of variables in TIMEMEASURES separated by the | symbol;

 

 

THE DEFINE COMMAND

 

 

DEFINE:

 

 

 

 

 

 

variable = mathematical expression;

 

 

 

IF (conditional statement) THEN transformation statements;

 

 

 

CUT variable name or list of variables (cutpoints);

 

 

 


THE ANALYSIS COMMAND

 

 

ANALYSIS:

 

 

 

 

 

TYPE =

GENERAL;

GENERAL;

 

BASIC;

 

 

MEANSTRUCTURE;

 

 

MISSING;

 

 

MCOHORT;

 

 

H1;

 

 

RANDOM;

 

 

COMPLEX;

 

 

MIXTURE;

BASIC;

MISSING;

RANDOM;

COMPLEX;

 

 

TWOLEVEL;

BASIC;

MISSING;

H1;

RANDOM;

MIXTURE;

 

 

EFA # #;

BASIC;

MISSING;

 

 

LOGISTIC;

 

ESTIMATOR =

ML;

depends on

 

MLM;

analysis type

 

MLMV;

 

 

MLR;

 

 

MLF;

 

 

MUML;

 

 

WLS;

 

 

WLSM;

 

 

WLSMV;

 

 

GLS;

 

 

ULS;

 


 

PARAMETERIZATION =

DELTA;

depends on

 

THETA;

analysis type

 

LOGIT;

 

 

LOGLINEAR;

 

CHOLESKY =

ON;

OFF;

depends on analysis type

ALGORITHM =

EMA;

depends on

 

EM;

analysis type

 

ODLL;

INTEGRATION;

 

INTEGRATION =

number of integration points;

STANDARD (number of integration points) ;

GAUSSHERMITE (number of integration points) ;

MONTECARLO (number of integration points);

STANDARD;

(15)

 

(15)

(500)

MCSEED =

random seed for Monte Carlo integration;

zero

ADAPTIVE =

ON;

OFF;

ON

INFORMATION =

OBSERVED;

depends on

 

EXPECTED;

analysis type

 

COMBINATION;

 

BOOTSTRAP =

number of bootstrap draws;

 

DIFFTEST =

file name;

 

STARTS =

number of initial stage starts number of final stage starts;

10 1

STITERATIONS =

number of initial stage iterations;

10

STCONVERGENCE =

initial stage convergence criterion;

1

STSCALE =

random start scale;

5

STSEED =

random seed for generating random starts;

0

OPTSEED =

random seed for analysis;

 

COVERAGE =

minimum covariance coverage with missing data;

.10

ITERATIONS =

maximum number of iterations for the Quasi-Newton algorithm for continuous outcomes;

1000

SDITERATIONS =

maximum number of steepest descent iterations for the Quasi-Newton algorithm for continuous outcomes;

20

H1ITERATIONS =

maximum number of iterations for unrestricted model with missing data;

2000

MITERATIONS =

number of iterations for the EM algorithm;

500

MCITERATIONS =

number of iterations for the M step of the EM algorithm for categorical latent variables;

1

MUITERATIONS =

number of iterations for the M step of the EM algorithm for censored, categorical, and count outcomes;

1


 

CONVERGENCE =

convergence criterion for the Quasi-Newton algorithm for continuous outcomes;

depends on analysis type

H1CONVERGENCE =

convergence criterion for unrestricted model with missing data;

.0001

LOGCRITERION =

likelihood convergence criterion for the EM algorithm;

depends on analysis type

MCONVERGENCE =

convergence criterion for the EM algorithm;

depends on analysis type

MCCONVERGENCE =

convergence criterion for the M step of the EM algorithm for categorical latent variables;

.000001

MUCONVERGENCE =

convergence criterion for the M step of the EM algorithm for censored, categorical, and count outcomes;

.000001

MIXC =

ITERATIONS;

ITERATIONS;

 

CONVERGENCE;

 

 

M step iteration termination based on number of iterations or convergence for categorical latent variables;

 

MIXU =

ITERATIONS;

ITERATIONS;

 

CONVERGENCE;

 

 

M step iteration termination based on number of iterations or convergence for censored, categorical, and count outcomes;

 

 

 

 

LOGHIGH =

max value for logit thresholds;

+15

LOGLOW =

min value for logit thresholds;

- 15

UCELLSIZE =

minimum expected cell size;

.01

VARIANCE =

minimum variance value;

.0001

MATRIX =

COVARIANCE;

COVARIANCE;

 

CORRELATION;

 

 


THE MODEL COMMAND

 

 

MODEL:

 

BY

short for measured by -- defines latent variables;

example: f1 BY y1-y5;

ON

short for regressed on -- defines regression relationships;

example: f1 ON x1-x9;

WITH

short for correlated with -- defines correlational relationships;

example: f1 WITH f2;

PWITH

short for correlated with defines paired correlational relationships;

example: f1 f2 f3 PWITH f4 f5 f6;

list of variables;

refers to variances and residual variances;

example: f1 y1-y9;

[list of variables];

refers to means, intercepts, thresholds;

example: [f1, y1-y9];

*

frees a parameter at a default value or a specific starting value;

example: y1* y2*.5;

@

fixes a parameter at a default value or a specific value;

example: y1@ y2@0;

(number)

constrains parameters to be equal;

example: f1 ON x1 (1);

f2 ON x2 (1);

variable$number

label for the threshold of a variable

variable#number

label for nominal observed or categorical latent variable

variable#1

label for censored or count inflation variable

variable#number

label for a latent class

(name)

label for a parameter

{list of variables};

refers to scale factors;

example: {y1-y9};

|

names and defines random effect variables;

example: s | y1 ON x1;

AT

short for measured at -- defines random effect variables

example: s | y1-y4 AT t1-t4;

XWITH

defines interactions between variables

MODEL INDIRECT:

 

IND

VIA

describes the relationships for which indirect and total effects are requested

describes a specific indirect effect or a set of indirect effects

describes a set of indirect effects that includes specific mediators

 

MODEL CONSTRAINT:

describes linear and non-linear constraints on parameters

 

 


 

MODEL:

describes the analysis model

MODEL label:

describes the group-specific model in multiple group analysis and the model for each categorical latent variable and combinations of categorical latent variables in mixture modeling

MODEL:

%OVERALL%

 

%class label%

 

describes the overall part of a mixture model

 

describes the class-specific part of a mixture model

MODEL:

%WITHIN%

%BETWEEN%

 

describes the within part of a two-level model

describes the between part of a two-level model

MODEL POPULATION:

describes the data generation model for a Monte Carlo study

MODEL POPULATION-label:

describes the group-specific data generation model in multiple group analysis and the data generation model for each categorical latent variable and combinations of categorical latent variables in mixture modeling for a Monte Carlo study

MODEL POPULATION:

%OVERALL%

 

%class label%

 

describes the overall data generation model of a mixture model

describes the class-specific data generation model of a mixture model

MODEL POPULATION:

%WITHIN%

%BETWEEN%

 

describes the within part of a two-level data generation model for a Monte Carlo study

describes the between part of a two-level data generation model for a Monte Carlo study


 

MODEL COVERAGE:

describes the population parameter values for a Monte Carlo study

MODEL COVERAGE-label:

describes the group-specific population parameter values in multiple group analysis and the population parameter values for each categorical latent variable and combinations of categorical latent variables in mixture modeling for a Monte Carlo study

MODEL COVERAGE:

%OVERALL%

 

%class label%

 

describes the overall population parameter values of a mixture model for a Monte Carlo study

describes the class-specific population values of a mixture model

MODEL COVERAGE:

%WITHIN%

%BETWEEN%

 

describes the within population parameter values for a two-level model for a Monte Carlo study

describes the between population parameter values for a two-level model for a Monte Carlo study

MODEL MISSING:

describes the missing data generation model for a Monte Carlo study

MODEL MISSING-label:

describes the group-specific missing data generation model for a Monte Carlo study

MODEL MISSING:

%OVERALL%

%class label%

 

describes the overall data generation model of a mixture model

describes the class-specific data generation model of a mixture model

 


 

THE OUTPUT COMMAND

 

 

OUTPUT:

 

 

 

 

 

 

SAMPSTAT;

 

 

STANDARDIZED;

 

 

RESIDUAL;

 

 

MODINDICES (minimum chi-square);

10

 

CINTERVAL;

CINTERVAL (SYMMETRIC);

CINTERVAL (BOOTSTRAP);

CINTERVAL (BCBOOTSTRAP);

SYMMETRIC

 

NOCHISQUARE;

 

 

NOSERROR;

 

 

H1SE;

 

 

H1TECH3;

 

 

PATTERNS;

 

 

FSCOEFFICIENT;

 

 

FSDETERMINACY;

 

 

TECH1;

 

 

TECH2;

 

 

TECH3;

 

 

TECH4;

 

 

TECH5;

 

 

TECH6;

 

 

TECH7;

 

 

TECH8;

 

 

TECH9;

 

 

TECH10;

 

 

TECH11;

 

 

TECH12;

 

 

TECH13;

 

 


THE SAVEDATA COMMAND

 

 

SAVEDATA:

 

 

 

 

 

FILE IS

file name;

 

SAMPLE IS

file name;

 

SIGB IS

file name;

 

RESULTS ARE

file name;

 

ESTIMATES ARE

file name;

 

DIFFTEST IS

file name;

 

TECH3 IS

file name;

 

TECH4 IS

file name;

 

 

 

 

FORMAT IS

format statement;

F10.3

 

FREE;

 

 

 

 

TYPE IS

COVARIANCE;

varies

 

CORRELATION;

 

 

 

 

RECORDLENGTH IS

characters per record;

1000

 

 

 

SAVE =

FSCORES;

 

 

CPROBABILITIES;

 

 

THE PLOT COMMAND

 

 

PLOT:

 

 

 

 

 

TYPE IS

PLOT1;

 

 

PLOT2;

 

 

PLOT3;

 

SERIES IS

list of variables in a series plus x-axis values;

 


THE MONTECARLO COMMAND

 

 

MONTECARLO:

 

 

 

 

 

NAMES =

names of variables;

 

NOBSERVATIONS =

number of observations;

 

NGROUPS =

number of groups;

1

NREPS =

number of replications;

1

SEED =

random seed for data generation;

 

GENERATE =

scales of dependent variables for data generation;

 

CUTPOINTS =

thresholds to be used for categorization of covariates

 

GENCLASSES =

names of categorical latent variables (number of latent classes used for data generation);

 

NCSIZES =

number of unique cluster sizes for each group separated by the | symbol;

 

CSIZES =

number (cluster size) for each group separated by the | symbol;

 

PATMISS =

missing data patterns and proportion missing for each dependent variable;

 

PATPROBS =

proportion for each missing data pattern;

 

MISSING =

names of dependent variables that have missing data;

 

CENSORED ARE

names and limits of censored-normal dependent variables;

 

CATEGORICAL ARE

names of ordered categorical dependent variables;

 

NOMINAL ARE

names of unordered categorical dependent variables;

 

COUNT ARE

names of count variables;

 

CLASSES =

names of categorical latent variables (number of latent classes used for model estimation);

 


 

TSCORES =

names, means, and standard deviations of observed variables with information on individually-varying times of observation;

 

WITHIN =

names of individual-level observed variables;

 

BETWEEN =

names of cluster-level observed variables;

 

POPULATION =

name of file containing true population parameter values for data generation;

 

COVERAGE =

name of file containing parameter values for computing parameter coverage;

 

REPSAVE =

numbers of the replications to save data from or ALL;

 

SAVE =

name of file in which generated data are stored;

 

RESULTS =

name of file in which analysis results are stored;