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      
                             describes a specific indirect effect or a set of indirect effects when there is no moderation;      
     IND                describes  a set of indirect effects that includes specific mediators;      
                         describes a specific indirect effect when there is moderation;      
     VIA            
            
     MOD            
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      
                         describes the class-specific data generation model for a mixture model      
     %class label%            
MODEL POPULATION:            
     %WITHIN%           describes the individual-level data generation model for a multilevel model      
                         describes the cluster-level data generation model for a two-level model      
     %BETWEEN%          describes the cluster-level data generation model for a three-level or cross-classified model      
            
     %BETWEEN label%            
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      
                             describes the class-specific population parameter values of a mixture model      
     %class label%            
MODEL COVERAGE:            
     %WITHIN%           describes the individual-level population parameter values for coverage      
                       describes the cluster-level population parameter values for a two-level model for coverage      
     %BETWEEN%          describes the cluster-level population parameter values for a three-level or cross-classified model for coverage      
            
     %BETWEEN label%            
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