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

LISTWISE =

ON;

OFF;

OFF

SWMATRIX =

file name;


VARIANCES =

CHECK;

NOCHECK;

CHECK



DATA IMPUTATION: IMPUTE =


NDATASETS = SAVE =


FORMAT = MODEL =


VALUES = ROUNDING =


THIN =


names of variables for which missing values will be imputed;

number of imputed data sets;

names of files in which imputed data sets are stored;

format statement; COVARIANCE; SEQUENTIAL; REGRESSION;

values imputed data can take;

number of decimals for imputed continuous variables;

k where every k-th imputation is saved;


5


F10.3

depends on analysis type


no restrictions 3


100

DATA WIDETOLONG: WIDE =

LONG =

IDVARIABLE = REPETITION =


names of old wide format variables; names of new long format variables; name of variable with ID information;

name of variable with repetition information;


DATA LONGTOWIDE: LONG =

WIDE = IDVARIABLE = REPETITION =


names of old long format variables; names of new wide format variables; name of variable with ID information;

name of variable with repetition information (values);



0, 1, 2, etc.

DATA TWOPART: NAMES =


CUTPOINT =


BINARY = CONTINUOUS = TRANSFORM =


names of variables used to create a set of binary and continuous variables;

value used to divide the original variables into a set of binary and continuous variables;

names of new binary variables; names of new continuous variables;

function to use to transform new continuous variables;


0


LOG

DATA MISSING: NAMES =


BINARY =


names of variables used to create a set of binary variables;

names of new binary variables;


TYPE =

MISSING;

SDROPOUT; DDROPOUT;


DESCRIPTIVE =

sets of variables for additional descriptive statistics separated by the | symbol;




DATA SURVIVAL: NAMES =


CUTPOINT =


BINARY =


names of variables used to create a set of binary event-history variables;

value used to create a set of binary event- history variables from a set of original variables;

names of new binary variables;

DATA COHORT: COHORT IS COPATTERN IS COHRECODE = TIMEMEASURES =


TNAMES =


name of cohort variable (values);

name of cohort/pattern variable (patterns); (old value = new value);

list of sets of variables separated by the | symbol;

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


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 of count variables (model);


DSURVIVAL ARE

names of discrete-time survival variables;


GROUPING IS

name of grouping variable (labels);


IDVARIABLE IS

name of ID variable;

_RECNUM;


FREQWEIGHT IS

name of frequency (case) weight variable;



names of observed variables with information



TSCORES ARE

on individually-varying times of observation;


AUXILIARY =

names of auxiliary variables; names of auxiliary variables (M);

names of auxiliary variables (R3STEP); names of auxiliary variables (R); names of auxiliary variables (BCH);

names of auxiliary variables (DU3STEP); names of auxiliary variables (DCATEGORICAL);

names of auxiliary variables (DE3STEP); names of auxiliary variables (DCONTINUOUS);

names of auxiliary variables (E);


CONSTRAINT =

names of observed variables that can be used in the MODEL CONSTRAINT command;


PATTERN IS

name of pattern variable (patterns);


STRATIFICATION IS

name of stratification variable;


CLUSTER IS

name of cluster variables;


WEIGHT IS

name of sampling weight variable;


WTSCALE IS

UNSCALED;

CLUSTER


CLUSTER;



ECLUSTER;


BWEIGHT

name of between-level sampling weight variable;


B2WEIGHT IS

name of the level 2 sampling weight variable;


B3WEIGHT IS

name of the level 3 sampling weight variable;


BWTSCALE IS

UNSCALED;

SAMPLE;

SAMPLE

REPWEIGHTS ARE

names of replicate weight variables;


SUBPOPULATION IS

conditional statement to select subpopulation;

all observations in data set

FINITE =

name of variable; name of variable (FPC);

name of variable (SFRACTION); name of variable (POPULATION);

FPC

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); names of variables (PRIORS);

MEMBERSHIP

WITHIN ARE

WITHIN ARE (label)

names of individual-level observed variables; names of individual-level observed variables;




BETWEEN ARE BETWEEN ARE (label)

names of cluster-level observed variables; names of cluster-level observed variables;


SURVIVAL ARE

names and time intervals for time-to-event

variables;


TIMECENSORED ARE


LAGGED ARE TINTERVAL IS

names and values of variables that contain right censoring information;

names of lagged variables (lag); name of time variable (interval);

(0 = NOT

1 = RIGHT)


THE DEFINE COMMAND


DEFINE:

variable = mathematical expression;

IF (conditional statement) THEN transformation statements;

_MISSING

variable = MEAN (list of variables);

variable = SUM (list of variables);

CUT variable or list of variables (cutpoints);

variable = CLUSTER_MEAN (variable);

CENTER variable or list of variables (GRANDMEAN); CENTER variable or list of variables (GROUPMEAN); CENTER variable or list of variables (GROUPMEAN

label);

STANDARDIZE variable or list of variables;

DO (number, number) expression;

DO ($, number, number) DO (#, number, number) expression;


THE ANALYSIS COMMAND


ANALYSIS:



TYPE =

GENERAL;

GENERAL


BASIC;



RANDOM;



COMPLEX;



MIXTURE;

BASIC;

RANDOM; COMPLEX;



TWOLEVEL; BASIC; RANDOM; MIXTURE; COMPLEX;



THREELEVEL; BASIC; RANDOM;

COMPLEX;



CROSSCLASSIFIED; RANDOM;


EFA # #;

BASIC;

MIXTURE;

COMPLEX;

TWOLEVEL;

EFA # # UW* # # UB*; EFA # # UW # # UB;

ESTIMATOR =

ML;

depends on


MLM;

analysis type


MLMV;



MLR;



MLF;



MUML;



WLS;



WLSM;



WLSMV;



ULS;



ULSMV;



GLS;



BAYES;



MODEL =

CONFIGURAL;



METRIC;



SCALAR;



NOMEANSTRUCTURE;

means


NOCOVARIANCES;

covariances


ALLFREE;

equal

ALIGNMENT =

FIXED;

last class



CONFIGURAL

FIXED (reference class CONFIGURAL);


FIXED (reference class BSEM);



FREE;

last class



CONFIGURAL

FREE (reference class CONFIGURAL);


FREE (reference class BSEM);


DISTRIBUTION =

NORMAL;

NORMAL


SKEWNORMAL;



TDISTRIBUTION;



SKEWT;


PARAMETERIZATION =

DELTA;

DELTA


THETA;



LOGIT;

LOGIT


LOGLINEAR;



PROBABILITY;

RESCOVARIANCES;


RESCOV

LINK =

LOGIT;

LOGIT


PROBIT;


ROTATION =

GEOMIN;

GEOMIN

(OBLIQUE value)


GEOMIN (OBLIQUE value);



GEOMIN (ORTHOGONAL value);



QUARTIMIN;

OBLIQUE


CF-VARIMAX;

OBLIQUE


CF-VARIMAX (OBLIQUE);



CF-VARIMAX (ORTHOGONAL);



CF-QUARTIMAX;

OBLIQUE


CF- QUARTIMAX (OBLIQUE);



CF- QUARTIMAX (ORTHOGONAL);



CF-EQUAMAX;

OBLIQUE


CF- EQUAMAX (OBLIQUE);



CF- EQUAMAX (ORTHOGONAL);



CF-PARSIMAX;

OBLIQUE


CF- PARSIMAX (OBLIQUE);



CF- PARSIMAX (ORTHOGONAL);




CF-FACPARSIM;

OBLIQUE


CF- FACPARSIM (OBLIQUE);


CF- FACPARSIM (ORTHOGONAL);


CRAWFER;

OBLIQUE 1/p


CRAWFER (OBLIQUE value);


CRAWFER (ORTHOGONAL value);


OBLIMIN;

OBLIQUE 0


OBLIMIN (OBLIQUE value);



OBLIMIN (ORTHOGONAL value);



VARIMAX;



PROMAX;



TARGET;



BI-GEOMIN;

OBLIQUE


BI-GEOMIN (OBLIQUE);



BI-GEOMIN (ORTHOGONAL);



BI-CF-QUARTIMAX;

OBLIQUE


BI-CF-QUARTIMAX (OBLIQUE);


BI-CF-QUARTIMAX (ORTHOGONAL);

ROWSTANDARDIZATION =

CORRELATION;

CORRELATION


KAISER;



COVARIANCE;


PARALLEL =

number;

0

REPSE =

BOOTSTRAP; JACKKNIFE; JACKKNIFE1; JACKKNIFE2; BRR;

FAY (#);




.3

BASEHAZARD =

ON;

OFF;

ON (EQUAL); ON (UNEQUAL); OFF (EQUAL);

OFF (UNEQUAL);

depends on analysis type EQUAL


EQUAL

CHOLESKY =

ON; OFF;

depends on analysis type

ALGORITHM =

EM;

depends on


EMA;

analysis type


FS; ODLL;

INTEGRATION;



INTEGRATION =

number of integration points;

STANDARD (number of integration points) ;


GAUSSHERMITE (number of integration points) ;

MONTECARLO (number of integration points);

STANDARD

depends on analysis type 15


depends on analysis type

MCSEED =

random seed for Monte Carlo integration;

0

ADAPTIVE =

ON; OFF;

ON

INFORMATION =

OBSERVED;

depends on


EXPECTED;

analysis type


COMBINATION;


BOOTSTRAP =

number of bootstrap draws;

number of bootstrap draws (STANDARD); number of bootstrap draws (RESIDUAL):

STANDARD

LRTBOOTSTRAP =

number of bootstrap draws for TECH14;

depends on analysis type

STARTS =

number of initial stage starts and number of final stage optimizations;

depends on analysis type

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;


K-1STARTS =

number of initial stage starts and number of final stage optimizations for the k-1 class

model for TECH14;

20 4

LRTSTARTS =

number of initial stage starts and number of final stage optimizations for TECH14;

0 0 40 8

RSTARTS =

number of random starts for the rotation

algorithm and number of factor solutions printed for exploratory factor analysis;

depends on analysis type

ASTARTS =

number of random starts for the alignment optimization;

30

H1STARTS =

Number of initial stage starts and number of final stage optimizations for the H1 model;

0 0

DIFFTEST =

file name;


MULTIPLIER =

file name;


COVERAGE =

minimum covariance coverage with missing data;

.10

ADDFREQUENCY =

value divided by sample size to add to cells with zero frequency;

.5


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

RITERATIONS =

maximum number of iterations in the rotation

algorithm for exploratory factor analysis;

10000

AITERATIONS =

maximum number of iterations in the

5000


alignment optimization;


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

RLOGCRITERION =

relative 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

RCONVERGENCE =

convergence criterion for the rotation algorithm

for exploratory factor analysis;

.00001

ACONVERGENCE =

convergence criterion for the derivatives of the alignment optimization;.

.001

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

SIMPLICITY =

SQRT;

SQRT


FOURTHRT;


TOLERANCE =

simplicity tolerance value;

.0001

METRIC=

REFGROUP;

REFGROUP


PRODUCT;


MATRIX =

COVARIANCE;

COVARIANCE


CORRELATION;


POINT =

MEDIAN;

MEAN; MODE;

MEDIAN

CHAINS =

number of MCMC chains;

2

BSEED =

seed for MCMC random number generation;

0

STVALUES =

UNPERTURBED; PERTURBED;

ML;

UNPERTURBED

PREDICTOR =

LATENT; OBSERVED;

depends on analysis type

ALGORITHM =

GIBBS; GIBBS (PX1); GIBBS (PX2); GIBBS (PX3); GIBBS (RW);

MH;

GIBBS (PX1)

BCONVERGENCE =

MCMC convergence criterion using Gelman- Rubin PSR;

.05

BITERATIONS =

maximum and minimum number of iterations

for each MCMC chain when Gelman-Rubin PSR is used;

50000 0

FBITERATIONS =

fixed number of iterations for each MCMC

chain when Gelman-Rubin PSR is not used;


THIN =

k where every k-th MCMC iteration is saved;

1

MDITERATIONS =

maximum number of iterations used to compute the Bayes multivariate mode;

10000

KOLMOGOROV =

number of draws from the MCMC chains;

100


PRIOR =

number of draws from the prior distribution;

1000

INTERACTIVE =

file name;


PROCESSORS =

# of processors # of threads;

1 1


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;

PON

short for regressed on -- defines paired regression relationships

example: f2 f3 PON f1 f2;

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 baseline hazard parameters

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


MOD

describes the relationships for which indirect and total effects are requested

describes a specific indirect effect or a set of indirect effects when there is no moderation;

describes a set of indirect effects that includes specific mediators;

describes a specific indirect effect when there is moderation;

MODEL CONSTRAINT: NEW

DO PLOT LOOP

describes linear and non-linear constraints on parameters assigns labels to parameters not in the analysis model; describes a do loop or double do loop;

describes y-axis variables; describes x-axis variables;

MODEL TEST: DO

describes restrictions on the analysis model for the Wald test describes a do loop or double do loop;

MODEL PRIORS: COVARIANCE DO

DIFFERENCE

specifies the prior distribution for the parameters

assigns a prior to the covariance between two parameters; describes a do loop or double do loop;

assigns priors to differences between 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%

%BETWEEN label%


describes the individual-level model

describes the cluster-level model for a two-level model describes the cluster-level model for a three-level or cross- classified model

MODEL POPULATION:

describes the data generation model

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

MODEL POPULATION:

%OVERALL%


%class label%


describes the overall data generation model for a mixture model

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


MODEL POPULATION:

%WITHIN%


%BETWEEN%


%BETWEEN label%


describes the individual-level data generation model for a multilevel model

describes the cluster-level data generation model for a two- level model

describes the cluster-level data generation model for a three- level or cross-classified model

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 parameter values of a mixture model

MODEL COVERAGE:

%WITHIN%


%BETWEEN%


%BETWEEN label%


describes the individual-level population parameter values for coverage

describes the cluster-level population parameter values for a two-level model for coverage

describes the cluster-level population parameter values for a three-level or cross-classified model for coverage

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;


CROSSTABS;

ALL

CROSSTABS (ALL);


CROSSTABS (COUNT);


CROSSTABS (%ROW);



CROSSTABS (%COLUMN);


CROSSTABS (%TOTAL);


STANDARDIZED;


STDYX;


STDY;


STDY;


STANDARDIZED (CLUSTER); STDYX (CLUSTER);

STDY (CLUSTER); STD (CLUSTER);


RESIDUAL;

RESIDUAL (CLUSTER);


MODINDICES (minimum chi-square);

MODINDICES (ALL);

MODINDICES (ALL minimum chi-square);

10


10

CINTERVAL;

CINTERVAL (SYMMETRIC); CINTERVAL (BOOTSTRAP); CINTERVAL (BCBOOTSTRAP); CINTERVAL (EQTAIL);

CINTERVAL (HPD);

SYMMETRIC


EQTAIL

SVALUES;


NOCHISQUARE;


NOSERROR;


H1SE;


H1TECH3; H1MODEL;

H1MODEL (COVARIANCE);

H1MODEL (SEQUENTIAL);


COVARIANCE

PATTERNS;


FSCOEFFICIENT;


FSDETERMINACY;

FSCOMPARISON;


BASEHAZARD;


LOGRANK; ALIGNMENT;


ENTROPY;


TECH1;


TECH2;


TECH3;


TECH4;

TECH4 (CLUSTER);


TECH5;


TECH6;



TECH7;

TECH8;

TECH9;

TECH10;

TECH11;

TECH12;

TECH13; TECH14; TECH15;

TECH16;


THE SAVEDATA COMMAND

image


SAVEDATA:



FILE IS

file name;


FORMAT IS

format statement;

F10.3


FREE;


MISSFLAG =

missing value flag;

*

RECORDLENGTH IS

characters per record;

1000

SAMPLE IS

file name;


COVARIANCE IS

file name;


SIGBETWEEN IS

file name;


SWMATRIX IS

file name;


RESULTS ARE STDRESULTS ARE

STDDISTRIBUTION IS

file name; file name;

file name;


ESTIMATES ARE

file name;


DIFFTEST IS

file name;


TECH3 IS

file name;


TECH4 IS

file name;


KAPLANMEIER IS

file name;


BASEHAZARD IS

file name;


ESTBASELINE IS

file name;


RESPONSE IS

file name;


MULTIPLIER IS

file name;


BPARAMETERS IS

file name;


RANKING IS

file name;


TYPE IS

COVARIANCE;

varies


CORRELATION;


SAVE =

FSCORES; FSCORES (# #);

LRESPONSES (#);




PROPENSITY; CPROBABILITIES; REPWEIGHTS; MAHALANOBIS; LOGLIKELIHOOD; INFLUENCE; COOKS;

BCHWEIGHTS;


FACTORS =

names of factors;


LRESPONSES =

names of latent response variables;

MFILE =

file name;


MNAMES =

names of variables in the data set;

MFORMAT =

format statement; FREE;

FREE

MMISSING =

Variable (#);

*;

.;


MSELECT =

names of variables;

all variables in MNAMES


THE PLOT COMMAND


PLOT:



TYPE IS

PLOT1;



PLOT2;



PLOT3;

SENSITIVITY;


SERIES IS

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

FACTORS ARE

names of factors (#);


LRESPONSES ARE

names of latent response variables (#);

OUTLIERS ARE

MAHALANOBIS; LOGLIKELIHOOD;

INFLUENCE; COOKS;


MONITOR IS

ON; OFF;

OFF


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;

0

GENERATE =

scale 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;


HAZARDC =

specifies the hazard for the censoring process;


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);

AUXILIARY =

names of auxiliary variables (R3STEP); names of auxiliary variables (R); names of auxiliary variables (BCH);

names of auxiliary variables (DU3STEP); names of auxiliary variables (DCATEGORICAL);

names of auxiliary variables (DE3STEP);

names of auxiliary variables (DCONTINUOUS);




names of auxiliary variables (E);

SURVIVAL =

names and time intervals for time-to-event variables;

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 population parameter

values for data generation;

COVERAGE =

name of file containing population parameter values for computing parameter coverage;

STARTING =

name of file containing parameter values for use as starting values for the analysis;

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;

BPARAMETERS =


LAGGED ARE

name of file in which Bayesian posterior parameter values are stored;

names of lagged variables (lag);