Fit Simple Mediation Model for Data Generated Using the Vale and Maurelli (1983) Approach (Skewness = 2, Kurtosis = 7) - Structural Equation Modeling

Fit Simple Mediation Model for Data Generated Using the Vale and Maurelli (1983) Approach (Skewness = 2, Kurtosis = 7) - Structural Equation Modeling

vm_mod_fit.sem.mlr(data, taskid)

Arguments

data	n by 3 matrix or data frame. data[, 1] correspond to values for x. data[, 2] correspond to values for m. data[, 3] correspond to values for y.
taskid	Numeric. Task ID.

See also

Other model fit functions: beta_fit.ols_simulation_summary(), beta_fit.ols_simulation(), beta_fit.ols_task_summary(), beta_fit.ols_task(), beta_fit.ols(), beta_fit.sem.mlr_simulation_summary(), beta_fit.sem.mlr_simulation(), beta_fit.sem.mlr_task_summary(), beta_fit.sem.mlr_task(), beta_fit.sem.mlr(), beta_std_fit.sem.mlr_simulation_summary(), beta_std_fit.sem.mlr_simulation(), beta_std_fit.sem.mlr_task_summary(), beta_std_fit.sem.mlr_task(), beta_std_fit.sem.mlr(), exp_fit.ols_simulation_summary(), exp_fit.ols_simulation(), exp_fit.ols_task_summary(), exp_fit.ols_task(), exp_fit.ols(), exp_fit.sem.mlr_simulation_summary(), exp_fit.sem.mlr_simulation(), exp_fit.sem.mlr_task_summary(), exp_fit.sem.mlr_task(), exp_fit.sem.mlr(), exp_std_fit.sem.mlr_simulation_summary(), exp_std_fit.sem.mlr_simulation(), exp_std_fit.sem.mlr_task_summary(), exp_std_fit.sem.mlr_task(), exp_std_fit.sem.mlr(), fit.cov(), fit.ols(), fit.sem.mlr(), fit.sem(), mvn_fit.ols_simulation_summary(), mvn_fit.ols_simulation(), mvn_fit.ols_task_summary(), mvn_fit.ols_task(), mvn_fit.ols(), mvn_fit.sem_simulation_summary(), mvn_fit.sem_simulation(), mvn_fit.sem_task_summary(), mvn_fit.sem_task(), mvn_fit.sem(), mvn_mar_10_fit.sem_simulation_summary(), mvn_mar_10_fit.sem_simulation(), mvn_mar_10_fit.sem_task_summary(), mvn_mar_10_fit.sem_task(), mvn_mar_10_fit.sem(), mvn_mar_20_fit.sem_simulation_summary(), mvn_mar_20_fit.sem_simulation(), mvn_mar_20_fit.sem_task_summary(), mvn_mar_20_fit.sem_task(), mvn_mar_20_fit.sem(), mvn_mar_30_fit.sem_simulation_summary(), mvn_mar_30_fit.sem_simulation(), mvn_mar_30_fit.sem_task_summary(), mvn_mar_30_fit.sem_task(), mvn_mar_30_fit.sem(), mvn_mcar_10_fit.sem_simulation_summary(), mvn_mcar_10_fit.sem_simulation(), mvn_mcar_10_fit.sem_task_summary(), mvn_mcar_10_fit.sem_task(), mvn_mcar_10_fit.sem(), mvn_mcar_20_fit.sem_simulation_summary(), mvn_mcar_20_fit.sem_simulation(), mvn_mcar_20_fit.sem_task_summary(), mvn_mcar_20_fit.sem_task(), mvn_mcar_20_fit.sem(), mvn_mcar_30_fit.sem_simulation_summary(), mvn_mcar_30_fit.sem_simulation(), mvn_mcar_30_fit.sem_task_summary(), mvn_mcar_30_fit.sem_task(), mvn_mcar_30_fit.sem(), mvn_mnar_10_fit.sem_simulation_summary(), mvn_mnar_10_fit.sem_simulation(), mvn_mnar_10_fit.sem_task_summary(), mvn_mnar_10_fit.sem_task(), mvn_mnar_10_fit.sem(), mvn_mnar_20_fit.sem_simulation_summary(), mvn_mnar_20_fit.sem_simulation(), mvn_mnar_20_fit.sem_task_summary(), mvn_mnar_20_fit.sem_task(), mvn_mnar_20_fit.sem(), mvn_mnar_30_fit.sem_simulation_summary(), mvn_mnar_30_fit.sem_simulation(), mvn_mnar_30_fit.sem_task_summary(), mvn_mnar_30_fit.sem_task(), mvn_mnar_30_fit.sem(), mvn_std_fit.sem_simulation_summary(), mvn_std_fit.sem_simulation(), mvn_std_fit.sem_task_summary(), mvn_std_fit.sem_task(), mvn_std_fit.sem(), vm_mod_fit.ols_simulation_summary(), vm_mod_fit.ols_simulation(), vm_mod_fit.ols_task_summary(), vm_mod_fit.ols_task(), vm_mod_fit.ols(), vm_mod_fit.sem.mlr_simulation_summary(), vm_mod_fit.sem.mlr_simulation(), vm_mod_fit.sem.mlr_task_summary(), vm_mod_fit.sem.mlr_task(), vm_mod_std_fit.sem.mlr_simulation_summary(), vm_mod_std_fit.sem.mlr_simulation(), vm_mod_std_fit.sem.mlr_task_summary(), vm_mod_std_fit.sem.mlr_task(), vm_mod_std_fit.sem.mlr(), vm_sev_fit.ols_simulation_summary(), vm_sev_fit.ols_simulation(), vm_sev_fit.ols_task_summary(), vm_sev_fit.ols_task(), vm_sev_fit.ols(), vm_sev_fit.sem.mlr_simulation_summary(), vm_sev_fit.sem.mlr_simulation(), vm_sev_fit.sem.mlr_task_summary(), vm_sev_fit.sem.mlr_task(), vm_sev_fit.sem.mlr(), vm_sev_std_fit.sem.mlr_simulation_summary(), vm_sev_std_fit.sem.mlr_simulation(), vm_sev_std_fit.sem.mlr_task_summary(), vm_sev_std_fit.sem.mlr_task(), vm_sev_std_fit.sem.mlr()

Author

Ivan Jacob Agaloos Pesigan

Examples

taskid <- 1
data <- mvn_dat(taskid = taskid)
vm_mod_fit.sem.mlr(data = data, taskid = taskid)
#>                    taskid                         n                      reps 
#>              1.000000e+00              1.000000e+03              5.000000e+03 
#>                    taudot                      beta                     alpha 
#>              1.414214e-01              7.140742e-01              7.140742e-01 
#>                 alphabeta                   sigma2x            sigma2epsilonm 
#>              5.099020e-01              2.250000e+02              1.102721e+02 
#>            sigma2epsilony                       mux                    deltam 
#>              7.332210e+01              1.000000e+02              2.859258e+01 
#>                    deltay                 taudothat                   betahat 
#>              1.445045e+01              1.365816e-01              6.893338e-01 
#>                  alphahat      sigma2hatepsilonyhat      sigma2hatepsilonmhat 
#>              7.194502e-01              6.942058e+01              1.133418e+02 
#>           alphahatbetahat            sehattaudothat              sehatbetahat 
#>              4.959414e-01              2.526638e-02              2.504732e-02 
#>             sehatalphahat sehatsigma2hatepsilonyhat sehatsigma2hatepsilonmhat 
#>              2.204111e-02              3.023512e+00              5.280324e+00 
vm_mod_fit.ols(data = data, taskid = taskid)
#>                    taskid                         n                      reps 
#>              1.000000e+00              1.000000e+03              5.000000e+03 
#>                    taudot                      beta                     alpha 
#>              1.414214e-01              7.140742e-01              7.140742e-01 
#>                 alphabeta                   sigma2x            sigma2epsilonm 
#>              5.099020e-01              2.250000e+02              1.102721e+02 
#>            sigma2epsilony                       mux                    deltam 
#>              7.332210e+01              1.000000e+02              2.859258e+01 
#>                    deltay                 deltayhat                 taudothat 
#>              1.445045e+01              1.727827e+01              1.365815e-01 
#>                   betahat                 deltamhat                  alphahat 
#>              6.893338e-01              2.732977e+01              7.194502e-01 
#>           alphahatbetahat            taudothatprime              betahatprime 
#>              4.959414e-01              1.413169e-01              7.162884e-01 
#>             alphahatprime alphahatprimebetahatprime                sigma2xhat 
#>              7.163817e-01              5.131359e-01              2.308498e+02 
#>      sigma2hatepsilonmhat      sigma2hatepsilonyhat                    muxhat 
#>              1.134554e+02              6.955983e+01              1.001261e+02 
#>            sehatdeltayhat            sehattaudothat              sehatbetahat 
#>              1.884741e+00              2.489188e-02              2.478571e-02 
#>            sehatdeltamhat             sehatalphahat     sehattaudothatprimetb 
#>              2.246213e+00              2.218018e-02              2.575489e-02 
#>       sehatbetahatprimetb      sehatalphahatprimetb  sehattaudothatprimedelta 
#>              2.575489e-02              2.208558e-02              2.571375e-02 
#>    sehatbetahatprimedelta   sehatalphahatprimedelta 
#>              2.240837e-02              1.540159e-02