Mplus MODEL

MODEL命令语法

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