R/mvn_mnar_30_unstd_fit.sem.R
mvn_mnar_30_fit.sem.RdFit Simple Mediation Model for Data Generated from a Multivariate Normal Distribution with Data Missing Not at Random - 30% - Structural Equation Modeling
mvn_mnar_30_fit.sem(data, taskid)
| data |
|
|---|---|
| taskid | Numeric. Task ID. |
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_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_fit.sem.mlr(),
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()
Ivan Jacob Agaloos Pesigan
taskid <- 1 data <- mvn_dat(taskid = taskid) # mvn_mnar_30_fit.sem ------------------------------------------------- mvn_mnar_30_fit.sem(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 1.223984e-01 7.173502e-01 #> alphahat sigma2hatepsilonyhat sigma2hatepsilonmhat #> 7.078380e-01 7.050633e+01 1.185207e+02 #> deltayhat deltamhat muxhat #> 1.607655e+01 2.972687e+01 9.966056e+01 #> sigma2xhat alphahatbetahat sehattaudothat #> 2.314382e+02 5.077677e-01 2.456229e-02 #> sehatbetahat sehatalphahat sehatsigma2hatepsilonyhat #> 2.440249e-02 2.264106e-02 3.154717e+00 #> sehatsigma2hatepsilonmhat sehatdeltayhat sehatdeltamhat #> 5.303059e+00 1.904106e+00 2.282559e+00 #> sehatmuxhat sehatsigma2xhat #> 4.813210e-01 1.035541e+01# mvn_fit.ols for comparison --------------------------------------- mvn_fit.ols(data = data[complete.cases(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 1.607655e+01 1.223984e-01 #> betahat deltamhat alphahat #> 7.173502e-01 2.972687e+01 7.078380e-01 #> alphahatbetahat taudothatprime betahatprime #> 5.077677e-01 1.245803e-01 7.349192e-01 #> alphahatprime alphahatprimebetahatprime sigma2xhat #> 7.032329e-01 5.168194e-01 2.316699e+02 #> sigma2hatepsilonmhat sigma2hatepsilonyhat muxhat #> 1.187582e+02 7.071848e+01 9.966056e+01 #> sehatdeltayhat sehattaudothat sehatbetahat #> 1.906015e+00 2.458691e-02 2.442696e-02 #> sehatdeltamhat sehatalphahat sehattaudothatprimetb #> 2.283702e+00 2.265240e-02 2.502521e-02 #> sehatbetahatprimetb sehatalphahatprimetb sehattaudothatprimedelta #> 2.502521e-02 2.250503e-02 2.499928e-02 #> sehatbetahatprimedelta sehatalphahatprimedelta #> 2.142595e-02 1.599217e-02# mvn_mnar_30_fit.sem with missing data ------------------------------- mnar_30 <- mvn_mnar_30_dat(data = data, taskid = taskid) mvn_mnar_30_fit.sem(data = mnar_30, 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 1.150162e-01 7.144738e-01 #> alphahat sigma2hatepsilonyhat sigma2hatepsilonmhat #> 7.042403e-01 7.032148e+01 1.187909e+02 #> deltayhat deltamhat muxhat #> 1.698413e+01 3.009520e+01 9.933404e+01 #> sigma2xhat alphahatbetahat sehattaudothat #> 2.323925e+02 5.031612e-01 2.885869e-02 #> sehatbetahat sehatalphahat sehatsigma2hatepsilonyhat #> 2.774547e-02 2.417019e-02 3.474532e+00 #> sehatsigma2hatepsilonmhat sehatdeltayhat sehatdeltamhat #> 5.918408e+00 2.109145e+00 2.417578e+00 #> sehatmuxhat sehatsigma2xhat #> 4.976214e-01 1.102223e+01