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; |
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); |