“Mplus LANGUAGE MPlus全部命令”的版本间的差异

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==TITLE==
 
==TITLE==
−
TITLE: 标题的具体内容;
+
<pre>TITLE: 标题的具体内容;</pre>
  
 
==DATA==
 
==DATA==
 +
<pre>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;                                                                                      CHECK
 +
                    NOCHECK;
 +
DATA IMPUTATION:           
 +
    IMPUTE =        names of variables for which missing values will be imputed;     
 +
                    number of imputed data sets;                                                                5
 +
    NDATASETS =    names of files in which imputed data sets are stored;     
 +
    SAVE =          COVARIANCE;                                                                                  COVARIANCE
 +
    MODEL =        SEQUENTIAL;     
 +
                    REGRESSION;     
 +
    VALUES =        values imputed data can take;                                                                no restrictions
 +
    ROUNDING =      number of decimals for imputed continuous variables;                                        3
 +
                    k where every k-th imputation is saved;                                                      100
 +
    THIN =           
 +
DATA WIDETOLONG:           
 +
    WIDE =          names of old wide format variables;     
 +
    LONG =          names of new long format variables;     
 +
    IDVARIABLE =    name of variable with ID information;     
 +
    REPETITION =    name of variable with repetition information;     
 +
DATA LONGTOWIDE:           
 +
    LONG =          names of old long format variables;     
 +
    WIDE =          names of new wide format variables;     
 +
    IDVARIABLE =    name of variable with ID information;     
 +
    REPETITION =    name of variable with repetition information
 +
                    (values);                                                                                    0, 1, 2, etc.
 +
DATA TWOPART:                 
 +
    NAMES =        names of variables used to create a set of binary and continuous variables;     
 +
    CUTPOINT =      value used to divide the original variables into a set of  binary and continuous variables;  0                       
 +
    BINARY =        names of new binary variables;     
 +
    CONTINUOUS =    names of new continuous variables;     
 +
    TRANSFORM =    function to use to transform new continuous variables;                                        LOG
 +
DATA MISSING:           
 +
    NAMES =        names of variables used to create a set of binary variables;     
 +
    BINARY =        names of new binary variables;     
 +
    TYPE =          MISSING;     
 +
                    SDROPOUT;     
 +
                    DDROPOUT;     
 +
    DESCRIPTIVE =  sets of variables for additional descriptive statistics separated by the | symbol;
 +
DATA SURVIVAL:
 +
    NAMES =        names of variables used to create a set of binary event-history variables;
 +
    CUTPOINT =      value used to create a set of binary event-history variables from a set of original variables;
 +
    BINARY =        names of new binary variables;
 +
DATA COHORT:
 +
    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;</pre>
  
 
==VARIABLE==
 
==VARIABLE==
 +
<pre>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;     
 +
FREQWEIGHT IS            name of frequency (case) weight variable;     
 +
TSCORES ARE              names of observed variables with information 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;                                                                        FPC
 +
                        name of variable (FPC);     
 +
                        name of variable (SFRACTION);     
 +
                        name of variable (POPULATION);     
 +
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;                                                                MEMBERSHIP
 +
                        names of variables (MEMBERSHIP);     
 +
                        names of variables (PROBABILITIES);     
 +
                        names of variables (PRIORS);     
 +
WITHIN ARE              names of individual-level observed variables;     
 +
WITHIN ARE (label)      names of individual-level observed variables;     
 +
           
 +
BETWEEN ARE              names of cluster-level observed variables;     
 +
BETWEEN ARE (label)      names of cluster-level observed variables;     
 +
SURVIVAL ARE            names and time intervals for time-to-event variables;     
 +
TIMECENSORED ARE        names and values of variables that contain right censoring information;                    (0 = NOT  1 = RIGHT)</pre>
  
 
==DEFINE==
 
==DEFINE==
 +
<pre>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 (#, #) expression;</pre>
  
 
==ANALYSIS==
 
==ANALYSIS==
 +
ANALYSIS命令有77条子命令。HTML版请访问：http://www.statmodel.com/HTML_UG/chapter16V8.htm
 +
 +
* TYPE =
 +
 +
* ESTIMATOR =
 +
 +
* MODEL =
 +
 +
* ALIGNMENT =
 +
 +
* DISTRIBUTION =
 +
 +
* PARAMETERIZATION =
 +
 +
* LINK =
 +
 +
* ROTATION =
 +
 +
* ROWSTANDARDIZATION =
 +
 +
* PARALLEL =
 +
 +
* REPSE =
 +
 +
* BASEHAZARD =
 +
 +
* CHOLESKY =
 +
 +
* ALGORITHM =
 +
 +
* INTEGRATION =
 +
 +
* MCSEED =
 +
 +
* ADAPTIVE =
 +
 +
* INFORMATION =
 +
 +
* BOOTSTRAP =
 +
 +
* LRTBOOTSTRAP =
 +
 +
* STARTS =
 +
 +
* STITERATIONS =
 +
 +
* STCONVERGENCE =
 +
 +
* STSCALE =
 +
 +
* STSEED =
 +
 +
* OPTSEED =
 +
 +
* K-1STARTS =
 +
 +
* LRTSTARTS =
 +
 +
* RSTARTS =
 +
 +
* ASTARTS =
 +
 +
* H1STARTS =
 +
 +
* DIFFTEST =
 +
 +
* MULTIPLIER =
 +
 +
* COVERAGE =
 +
 +
* ADDFREQUENCY =
 +
 +
* ITERATIONS =
 +
 +
* SDITERATIONS =
 +
 +
* H1ITERATIONS =
 +
 +
* MITERATIONS =
 +
 +
* MCITERATIONS =
 +
 +
* MUITERATIONS =
 +
 +
* RITERATIONS =
 +
 +
* AITERATIONS =
 +
 +
* CONVERGENCE =
 +
 +
* H1CONVERGENCE =
 +
 +
* LOGCRITERION =
 +
 +
* RLOGCRITERION =
 +
 +
* MCONVERGENCE =
 +
 +
* MCCONVERGENCE =
 +
 +
* MUCONVERGENCE =
 +
 +
* RCONVERGENCE =
 +
 +
* ACONVERGENCE =
 +
 +
* MIXC =
 +
 +
* MIXU =
 +
 +
* LOGHIGH =
 +
 +
* LOGLOW =
 +
 +
* UCELLSIZE =
 +
 +
* VARIANCE  =
 +
 +
* SIMPLICITY =
 +
 +
* TOLERANCE =
 +
 +
* METRIC=
 +
 +
* MATRIX =
 +
 +
* POINT =
 +
 +
* CHAINS =
 +
 +
* BSEED =
 +
 +
* STVALUES =
 +
 +
* MEDIATOR =
 +
 +
* ALGORITHM =
 +
 +
* BCONVERGENCE =
 +
 +
* BITERATIONS =
 +
 +
* FBITERATIONS =
 +
 +
* THIN =
 +
 +
* MDITERATIONS =
 +
 +
* KOLMOGOROV =
 +
 +
* PRIOR =
 +
 +
* INTERACTIVE =
 +
 +
* PROCESSORS =
  
 
==MODEL==
 
==MODEL==
 +
<pre>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:              describes the relationships for which indirect and total effects are requested
 +
      IND                    describes a specific indirect effect or a set of indirect effects when there is no moderation;
 +
      VIA                    describes  a set of indirect effects that includes specific mediators;
 +
      MOD                    describes a specific indirect effect when there is moderation;
 +
MODEL CONSTRAINT:            describes linear and non-linear constraints on parameters
 +
    NEW                    assigns labels to parameters not in the analysis model;
 +
    DO                      describes a do loop or double do loop;
 +
    PLOT                    describes y-axis variables;
 +
    LOOP                    describes x-axis variables;
 +
MODEL TEST:                  describes restrictions on the analysis model for the Wald test
 +
    DO                      describes a do loop or double do loop;
 +
MODEL PRIORS:                specifies the prior distribution for the parameters
 +
    COVARIANCE              assigns a prior to the covariance between two parameters;
 +
    DO                      describes a do loop or double do loop;
 +
    DIFFERENCE              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%              describes the overall part of a mixture model
 +
    %class label%          describes the class-specific part of a mixture model
 +
MODEL:         
 +
    %WITHIN%              describes the individual-level model
 +
    %BETWEEN%              describes the cluster-level model for a two-level model
 +
    %BETWEEN label%        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%              describes the overall data generation model for a  mixture model
 +
    %class label%          describes the class-specific data generation model for a mixture model             
 +
MODEL POPULATION:     
 +
    %WITHIN%              describes the individual-level data generation model for a multilevel model
 +
    %BETWEEN%              describes the cluster-level data generation model for a two-level model
 +
    %BETWEEN label%        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%              describes the overall population parameter values of a mixture model for a Monte Carlo study
 +
    %class label%          describes the class-specific population parameter values of a mixture model   
 +
MODEL COVERAGE:     
 +
    %WITHIN%              describes the individual-level population parameter values for coverage
 +
    %BETWEEN%              describes the cluster-level population parameter values for a two-level model for coverage
 +
    %BETWEEN label%        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 Carlostudy
 +
MODEL MISSING-label:        describes the group-specific missing data generation model for aMonte Carlo study
 +
MODEL MISSING:       
 +
    %OVERALL%              describes the overall data generation model of a mixture model
 +
    %class label%          describes the class-specific data generation model of a mixture model</pre>
  
 
==OUTPUT==
 
==OUTPUT==
 +
<pre>OUTPUT:
 +
SAMPSTAT;
 +
CROSSTABS;                              ALL
 +
CROSSTABS (ALL);
 +
CROSSTABS (COUNT);
 +
CROSSTABS (%ROW);
 +
CROSSTABS (%COLUMN);
 +
CROSSTABS (%TOTAL);
 +
STANDARDIZED;                            ALL
 +
STANDARDIZED (ALL);
 +
STANDARDIZED (STDYX);
 +
STANDARDIZED (STDY);
 +
STANDARDIZED (STD);
 +
RESIDUAL;
 +
MODINDICES (minimum chi-square);10
 +
MODINDICES (ALL);
 +
MODINDICES (ALL minimum chi-square);10
 +
CINTERVAL;                              SYMMETRIC
 +
CINTERVAL (SYMMETRIC);
 +
CINTERVAL (BOOTSTRAP);
 +
CINTERVAL (BCBOOTSTRAP);
 +
CINTERVAL (EQTAIL);                      EQTAIL
 +
CINTERVAL (HPD);
 +
SVALUES;
 +
NOCHISQUARE;
 +
NOSERROR;
 +
H1SE;
 +
H1TECH3;
 +
H1MODEL;                                COVARIANCE
 +
H1MODEL (COVARIANCE);
 +
H1MODEL (SEQUENTIAL);
 +
PATTERNS;
 +
FSCOEFFICIENT;
 +
FSDETERMINACY;
 +
BASEHAZARD;
 +
LOGRANK;
 +
ALIGNMENT;
 +
ENTROPY;
 +
TECH1;
 +
TECH2;
 +
TECH3;
 +
TECH4;
 +
TECH5;
 +
TECH6;
 +
TECH7;
 +
TECH8;
 +
TECH9;
 +
TECH10;
 +
TECH11;
 +
TECH12;
 +
TECH13;
 +
TECH14;
 +
TECH15;
 +
TECH16;</pre>
  
 
==SAVEDATA==
 
==SAVEDATA==
 +
<pre>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 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 (#);
 +
        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</pre>
  
 
==PLOT==
 
==PLOT==
 +
<pre>PLOT:    
 +
TYPE IS         PLOT1;
 +
        PLOT2;
 +
        PLOT3;
 +
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;</pre>
  
 
==MONTECARLO==
 
==MONTECARLO==
 +
<pre>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 = name of file in which Bayesian posterior parameter values are stored; </pre>

2020年4月26日 (日) 20:15的最新版本

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TITLE

TITLE: 标题的具体内容;

DATA

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;                                                                                       CHECK
                     NOCHECK;
DATA IMPUTATION:            
     IMPUTE =        names of variables for which missing values will be imputed;      
                     number of imputed data sets;                                                                 5
     NDATASETS =     names of files in which imputed data sets are stored;      
     SAVE =          COVARIANCE;                                                                                  COVARIANCE
     MODEL =         SEQUENTIAL;      
                     REGRESSION;      
     VALUES =        values imputed data can take;                                                                no restrictions
     ROUNDING =      number of decimals for imputed continuous variables;                                         3
                     k where every k-th imputation is saved;                                                      100
     THIN =            
DATA WIDETOLONG:            
     WIDE =          names of old wide format variables;      
     LONG =          names of new long format variables;      
     IDVARIABLE =    name of variable with ID information;      
     REPETITION =    name of variable with repetition information;      
DATA LONGTOWIDE:            
     LONG =          names of old long format variables;      
     WIDE =          names of new wide format variables;      
     IDVARIABLE =    name of variable with ID information;      
     REPETITION =    name of variable with repetition information 
                     (values);                                                                                    0, 1, 2, etc.
DATA TWOPART:                  
     NAMES =         names of variables used to create a set of binary and continuous variables;      
     CUTPOINT =      value used to divide the original variables into a set of  binary and continuous variables;  0                        
     BINARY =        names of new binary variables;      
     CONTINUOUS =    names of new continuous variables;      
     TRANSFORM =     function to use to transform new continuous variables;                                        LOG
DATA MISSING:            
     NAMES =         names of variables used to create a set of binary variables;      
     BINARY =        names of new binary variables;      
     TYPE =          MISSING;      
                     SDROPOUT;      
                     DDROPOUT;      
     DESCRIPTIVE =   sets of variables for additional descriptive statistics separated by the | symbol; 
DATA SURVIVAL:
     NAMES =         names of variables used to create a set of binary event-history variables;
     CUTPOINT =      value used to create a set of binary event-history variables from a set of original variables;
     BINARY =        names of new binary variables;
DATA COHORT:
     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;

VARIABLE

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;      
FREQWEIGHT IS            name of frequency (case) weight variable;      
TSCORES ARE              names of observed variables with information 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;                                                                        FPC
                         name of variable (FPC);      
                         name of variable (SFRACTION);      
                         name of variable (POPULATION);      
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;                                                                MEMBERSHIP
                         names of variables (MEMBERSHIP);      
                         names of variables (PROBABILITIES);      
                         names of variables (PRIORS);      
WITHIN ARE               names of individual-level observed variables;      
WITHIN ARE (label)       names of individual-level observed variables;      
            
BETWEEN ARE              names of cluster-level observed variables;      
BETWEEN ARE (label)      names of cluster-level observed variables;      
SURVIVAL ARE             names and time intervals for time-to-event variables;      
TIMECENSORED ARE         names and values of variables that contain right censoring information;                    (0 = NOT   1 = RIGHT)

DEFINE

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 (#, #) expression;

ANALYSIS

ANALYSIS命令有77条子命令。HTML版请访问：http://www.statmodel.com/HTML_UG/chapter16V8.htm

  • TYPE =
  • ESTIMATOR =
  • MODEL =
  • ALIGNMENT =
  • DISTRIBUTION =
  • PARAMETERIZATION =
  • LINK =
  • ROTATION =
  • ROWSTANDARDIZATION =
  • PARALLEL =
  • REPSE =
  • BASEHAZARD =
  • CHOLESKY =
  • ALGORITHM =
  • INTEGRATION =
  • MCSEED =
  • ADAPTIVE =
  • INFORMATION =
  • BOOTSTRAP =
  • LRTBOOTSTRAP =
  • STARTS =
  • STITERATIONS =
  • STCONVERGENCE =
  • STSCALE =
  • STSEED =
  • OPTSEED =
  • K-1STARTS =
  • LRTSTARTS =
  • RSTARTS =
  • ASTARTS =
  • H1STARTS =
  • DIFFTEST =
  • MULTIPLIER =
  • COVERAGE =
  • ADDFREQUENCY =
  • ITERATIONS =
  • SDITERATIONS =
  • H1ITERATIONS =
  • MITERATIONS =
  • MCITERATIONS =
  • MUITERATIONS =
  • RITERATIONS =
  • AITERATIONS =
  • CONVERGENCE =
  • H1CONVERGENCE =
  • LOGCRITERION =
  • RLOGCRITERION =
  • MCONVERGENCE =
  • MCCONVERGENCE =
  • MUCONVERGENCE =
  • RCONVERGENCE =
  • ACONVERGENCE =
  • MIXC =
  • MIXU =
  • LOGHIGH =
  • LOGLOW =
  • UCELLSIZE =
  • VARIANCE =
  • SIMPLICITY =
  • TOLERANCE =
  • METRIC=
  • MATRIX =
  • POINT =
  • CHAINS =
  • BSEED =
  • STVALUES =
  • MEDIATOR =
  • ALGORITHM =
  • BCONVERGENCE =
  • BITERATIONS =
  • FBITERATIONS =
  • THIN =
  • MDITERATIONS =
  • KOLMOGOROV =
  • PRIOR =
  • INTERACTIVE =
  • PROCESSORS =

MODEL

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:              describes the relationships for which indirect and total effects are requested
      IND                    describes a specific indirect effect or a set of indirect effects when there is no moderation;
      VIA                    describes  a set of indirect effects that includes specific mediators;
      MOD                    describes a specific indirect effect when there is moderation;
MODEL CONSTRAINT:            describes linear and non-linear constraints on parameters
     NEW                     assigns labels to parameters not in the analysis model;
     DO                      describes a do loop or double do loop;
     PLOT                    describes y-axis variables;
     LOOP                    describes x-axis variables;
MODEL TEST:                  describes restrictions on the analysis model for the Wald test
     DO                      describes a do loop or double do loop;
MODEL PRIORS:                specifies the prior distribution for the parameters
     COVARIANCE              assigns a prior to the covariance between two parameters;
     DO                      describes a do loop or double do loop;
     DIFFERENCE              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%              describes the overall part of a mixture model
     %class label%          describes the class-specific part of a mixture model
MODEL:          
     %WITHIN%               describes the individual-level model
     %BETWEEN%              describes the cluster-level model for a two-level model
     %BETWEEN label%        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%              describes the overall data generation model for a  mixture model
     %class label%          describes the class-specific data generation model for a mixture model              
MODEL POPULATION:      
     %WITHIN%               describes the individual-level data generation model for a multilevel model
     %BETWEEN%              describes the cluster-level data generation model for a two-level model
     %BETWEEN label%        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%              describes the overall population parameter values of a mixture model for a Monte Carlo study
     %class label%          describes the class-specific population parameter values of a mixture model     
MODEL COVERAGE:      
     %WITHIN%               describes the individual-level population parameter values for coverage
     %BETWEEN%              describes the cluster-level population parameter values for a two-level model for coverage
     %BETWEEN label%        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 Carlostudy
MODEL MISSING-label:         describes the group-specific missing data generation model for aMonte Carlo study
MODEL MISSING:        
     %OVERALL%              describes the overall data generation model of a mixture model
     %class label%          describes the class-specific data generation model of a mixture model

OUTPUT

OUTPUT:		
	SAMPSTAT;
	CROSSTABS;                               ALL
	CROSSTABS (ALL);
	CROSSTABS (COUNT);
	CROSSTABS (%ROW);
	CROSSTABS (%COLUMN);
	CROSSTABS (%TOTAL);
	STANDARDIZED;                            ALL
	STANDARDIZED (ALL);
	STANDARDIZED (STDYX);
	STANDARDIZED (STDY);
	STANDARDIZED (STD);
	RESIDUAL;
	MODINDICES (minimum chi-square);10
	MODINDICES (ALL);
	MODINDICES (ALL minimum chi-square);10
	CINTERVAL;                               SYMMETRIC
	CINTERVAL (SYMMETRIC);
	CINTERVAL (BOOTSTRAP);
	CINTERVAL (BCBOOTSTRAP);
	CINTERVAL (EQTAIL);                      EQTAIL
	CINTERVAL (HPD);
	SVALUES;
	NOCHISQUARE;
	NOSERROR;
	H1SE;
	H1TECH3;
	H1MODEL;                                 COVARIANCE
	H1MODEL (COVARIANCE);
	H1MODEL (SEQUENTIAL);
	PATTERNS;
	FSCOEFFICIENT;
	FSDETERMINACY;
	BASEHAZARD;
	LOGRANK;
	ALIGNMENT;
	ENTROPY;
	TECH1;
	TECH2;
	TECH3;
	TECH4;
	TECH5;
	TECH6;
	TECH7;
	TECH8;
	TECH9;
	TECH10;
	TECH11;
	TECH12;
	TECH13;
	TECH14;
	TECH15;
	TECH16;

SAVEDATA

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	 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 (#);
         	 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

PLOT

PLOT:		     		
TYPE IS	        PLOT1;	
	        PLOT2;	
	        PLOT3;	
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;

MONTECARLO

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 =	 name of file in which Bayesian posterior parameter values are stored;
取自“http://www.obhrm.net/index.php?title=Mplus_LANGUAGE_MPlus全部命令&oldid=7571”