R/results_mvn_fit.sem.R
results_mvn_fit.sem.RdResults: Simple Mediation Model - Multivariate Normal Distribution - Complete Data - Fit Structural Equation Modeling
results_mvn_fit.sem
A data frame with the following variables
Simulation task identification number.
Sample size.
Monte Carlo replications.
Population slope of path from x to y \(\left( \dot{\tau} \right)\).
Population slope of path from m to y \(\left( \beta \right)\).
Population slope of path from x to m \(\left( \alpha \right)\).
Population indirect effect of x on y through m \(\left( \alpha \beta \right)\).
Population variance of x \(\left( \sigma_{x}^{2} \right)\).
Population error variance of m \(\left( \sigma_{\varepsilon_{m}}^{2} \right)\).
Population error variance of y \(\left( \sigma_{\varepsilon_{y}}^{2} \right)\).
Population mean of x \(\left( \mu_x \right)\).
Population intercept of m \(\left( \delta_m \right)\).
Population intercept of y \(\left( \delta_y \right)\).
Mean of estimated slope of path from x to y \(\left( \hat{\dot{\tau}} \right)\).
Mean of estimated slope of path from m to y \(\left( \hat{\beta} \right)\).
Mean of estimated slope of path from x to m \(\left( \hat{\alpha} \right)\).
Mean of estimated error variance of y \(\left( \hat{\sigma}_{\varepsilon_{y}}^{2} \right)\).
Mean of estimated error variance of m \(\left( \hat{\sigma}_{\varepsilon_{m}}^{2} \right)\).
Mean of estimated indirect effect of x on y through m \(\left( \hat{\alpha} \hat{\beta} \right)\).
Mean of estimated standard error of \(\hat{\dot{\tau}}\).
Mean of estimated standard error of \(\hat{\beta}\).
Mean of estimated standard error of \(\hat{\alpha}\).
Mean of estimated standard error of error variance of y \(\left( \hat{\sigma}_{\varepsilon_{y}}^{2} \right)\).
Mean of estimated standard error of error variance of m \(\left( \hat{\sigma}_{\varepsilon_{m}}^{2} \right)\).
Population parameter \(\alpha \beta\).
Variance of estimated slope of path from x to y \(\left( \hat{\dot{\tau}} \right)\).
Variance of estimated slope of path from m to y \(\left( \hat{\beta} \right)\).
Variance of estimated slope of path from x to m \(\left( \hat{\alpha} \right)\).
Variance of estimated indirect effect of x on y through m \(\left( \hat{\alpha} \hat{\beta} \right)\).
Standard deviation of estimated slope of path from x to y \(\left( \hat{\dot{\tau}} \right)\).
Standard deviation of estimated slope of path from m to y \(\left( \hat{\beta} \right)\).
Standard deviation of estimated slope of path from x to m \(\left( \hat{\alpha} \right)\).
Standard deviation of estimated indirect effect of x on y through m \(\left( \hat{\alpha} \hat{\beta} \right)\).
Skewness of estimated slope of path from x to y \(\left( \hat{\dot{\tau}} \right)\).
Skewness of estimated slope of path from m to y \(\left( \hat{\beta} \right)\).
Skewness of estimated slope of path from x to m \(\left( \hat{\alpha} \right)\).
Skewness of estimated indirect effect of x on y through m \(\left( \hat{\alpha} \hat{\beta} \right)\).
Excess kurtosis of estimated slope of path from x to y \(\left( \hat{\dot{\tau}} \right)\).
Excess kurtosis of estimated slope of path from m to y \(\left( \hat{\beta} \right)\).
Excess kurtosis of estimated slope of path from x to m \(\left( \hat{\alpha} \right)\).
Excess kurtosis of estimated indirect effect of x on y through m \(\left( \hat{\alpha} \hat{\beta} \right)\).
Bias of estimated slope of path from x to y \(\left( \hat{\dot{\tau}} \right)\).
Bias of estimated slope of path from m to y \(\left( \hat{\beta} \right)\).
Bias of estimated slope of path from x to m \(\left( \hat{\alpha} \right)\).
Bias of estimated indirect effect of x on y through m \(\left( \hat{\alpha} \hat{\beta} \right)\).
Mean square error of estimated slope of path from x to y \(\left( \hat{\dot{\tau}} \right)\).
Mean square error of estimated slope of path from m to y \(\left( \hat{\beta} \right)\).
Mean square error of estimated slope of path from x to m \(\left( \hat{\alpha} \right)\).
Mean square error of estimated indirect effect of x on y through m \(\left( \hat{\alpha} \hat{\beta} \right)\).
Root mean square error of estimated slope of path from x to y \(\left( \hat{\dot{\tau}} \right)\).
Root mean square error of estimated slope of path from m to y \(\left( \hat{\beta} \right)\).
Root mean square error of estimated slope of path from x to m \(\left( \hat{\alpha} \right)\).
Root mean square error of estimated indirect effect of x on y through m \(\left( \hat{\alpha} \hat{\beta} \right)\).
Type of missingness.
Standardized vs. unstandardize indirect effect.
Method used. Fit in this case.
Sample size labels.
\(\alpha\) labels.
\(\beta\) labels.
\(\dot{\tau}\) labels.
\(\theta\) labels.
The simple mediation model is given by $$ y_i = \delta_y + \dot{\tau} x_i + \beta m_i + \varepsilon_{y_{i}} $$
$$ m_i = \delta_m + \alpha x_i + \varepsilon_{m_{i}} $$
The parameters for the mean structure are $$ \boldsymbol{\theta}_{\text{mean structure}} = \left\{ \mu_x, \delta_m, \delta_y \right\} . $$
The parameters for the covariance structure are $$ \boldsymbol{\theta}_{\text{covariance structure}} = \left\{ \dot{\tau}, \beta, \alpha, \sigma_{x}^{2}, \sigma_{\varepsilon_{m}}^{2}, \sigma_{\varepsilon_{y}}^{2} \right\} . $$
Other results:
results_beta_fit.ols,
results_beta_ols_mc.mvn_ci,
results_exp_fit.ols,
results_exp_ols_mc.mvn_ci,
results_mvn_fit.ols,
results_mvn_mar_fit.sem,
results_mvn_mar_mc.mvn_ci,
results_mvn_mar_nb.fiml_ci,
results_mvn_mar_pb.mvn_ci,
results_mvn_mcar_fit.sem,
results_mvn_mcar_mc.mvn_ci,
results_mvn_mcar_nb.fiml_ci,
results_mvn_mcar_pb.mvn_ci,
results_mvn_mnar_fit.sem,
results_mvn_mnar_mc.mvn_ci,
results_mvn_mnar_nb.fiml_ci,
results_mvn_nb_ci,
results_mvn_ols_mc.mvn_ci,
results_mvn_pb.mvn_ci,
results_mvn_sem_mc.mvn_ci,
results_vm_mod_fit.ols,
results_vm_mod_fit.sem.mlr,
results_vm_mod_nb_ci,
results_vm_mod_ols_mc.mvn_ci,
results_vm_mod_pb.mvn_ci,
results_vm_mod_sem_mc.mvn_ci,
results_vm_sev_fit.ols,
results_vm_sev_fit.sem.mlr,
results_vm_sev_nb_ci,
results_vm_sev_ols_mc.mvn_ci,
results_vm_sev_pb.mvn_ci,
results_vm_sev_sem_mc.mvn_ci
#> taskid n reps taudot beta alpha alphabeta sigma2x #> 1 1 1000 5000 0.1414214 0.7140742 0.7140742 0.509902 225 #> 2 2 500 5000 0.1414214 0.7140742 0.7140742 0.509902 225 #> 3 3 250 5000 0.1414214 0.7140742 0.7140742 0.509902 225 #> 4 4 200 5000 0.1414214 0.7140742 0.7140742 0.509902 225 #> 5 5 150 5000 0.1414214 0.7140742 0.7140742 0.509902 225 #> 6 6 100 5000 0.1414214 0.7140742 0.7140742 0.509902 225 #> sigma2epsilonm sigma2epsilony mux deltam deltay taudothat betahat #> 1 110.2721 73.3221 100 28.59258 14.45045 0.1414002 0.7137815 #> 2 110.2721 73.3221 100 28.59258 14.45045 0.1412213 0.7144869 #> 3 110.2721 73.3221 100 28.59258 14.45045 0.1405349 0.7142294 #> 4 110.2721 73.3221 100 28.59258 14.45045 0.1414157 0.7136251 #> 5 110.2721 73.3221 100 28.59258 14.45045 0.1402906 0.7151770 #> 6 110.2721 73.3221 100 28.59258 14.45045 0.1414066 0.7147568 #> alphahat sigma2hatepsilonyhat sigma2hatepsilonmhat alphahatbetahat #> 1 0.7141127 73.11720 110.1368 0.5097122 #> 2 0.7143225 72.97087 110.1695 0.5103810 #> 3 0.7148167 72.75003 109.8633 0.5105478 #> 4 0.7140369 72.72633 109.6017 0.5095902 #> 5 0.7136012 72.50357 109.7938 0.5103061 #> 6 0.7126990 71.73672 109.2348 0.5094397 #> sehattaudothat sehatbetahat sehatalphahat sehatsigma2hatepsilonyhat #> 1 0.02579011 0.02579128 0.02214618 3.271537 #> 2 0.03649417 0.03647070 0.03136849 4.619705 #> 3 0.05171566 0.05167254 0.04439642 6.520014 #> 4 0.05786239 0.05789197 0.04958416 7.290883 #> 5 0.06678267 0.06680260 0.05735380 8.400038 #> 6 0.08183516 0.08187548 0.07043264 10.196214 #> sehatsigma2hatepsilonmhat theta taudothat_var betahat_var alphahat_var #> 1 4.927930 0.5097122 0.0006766288 0.0006872606 0.0004819306 #> 2 6.974710 0.5103810 0.0013166380 0.0013428910 0.0009876333 #> 3 9.846182 0.5105478 0.0027661321 0.0027626204 0.0019793815 #> 4 10.987677 0.5095902 0.0034041240 0.0033470956 0.0024631209 #> 5 12.720370 0.5103061 0.0045092302 0.0044723630 0.0033331922 #> 6 15.525964 0.5094397 0.0070705337 0.0068454895 0.0050008717 #> alphahatbetahat_var taudothat_sd betahat_sd alphahat_sd alphahatbetahat_sd #> 1 0.0005886977 0.02601209 0.02621566 0.02195292 0.02426309 #> 2 0.0011984475 0.03628551 0.03664548 0.03142663 0.03461860 #> 3 0.0024342226 0.05259403 0.05256064 0.04449024 0.04933784 #> 4 0.0029990743 0.05834487 0.05785409 0.04962984 0.05476380 #> 5 0.0039483459 0.06715080 0.06687573 0.05773380 0.06283587 #> 6 0.0061032551 0.08408647 0.08273747 0.07071684 0.07812333 #> taudothat_skew betahat_skew alphahat_skew alphahatbetahat_skew taudothat_kurt #> 1 -0.07881651 0.009080082 -0.013253940 0.1091220 -0.059229618 #> 2 -0.01040010 0.010047698 0.002196431 0.1189809 -0.046067429 #> 3 -0.04734244 0.049517179 -0.052709736 0.1613471 0.032860579 #> 4 0.02098275 -0.034159406 0.016526797 0.1209768 0.020982937 #> 5 -0.03424818 0.038785609 0.029762105 0.1853423 0.008660779 #> 6 0.02548358 -0.024592439 -0.020559340 0.1978930 0.131789922 #> betahat_kurt alphahat_kurt alphahatbetahat_kurt taudothat_bias betahat_bias #> 1 -0.07110273 -0.063011747 -0.020512686 -2.116680e-05 -0.0002926596 #> 2 0.04769102 0.008850292 0.127688124 -2.000503e-04 0.0004126729 #> 3 -0.11791070 -0.039487006 -0.072165711 -8.864546e-04 0.0001552383 #> 4 -0.12777229 -0.065638799 0.001530856 -5.698084e-06 -0.0004491216 #> 5 -0.05971791 0.173845713 -0.070674357 -1.130723e-03 0.0011028111 #> 6 0.01954480 0.056486119 -0.044210038 -1.473057e-05 0.0006825693 #> alphahat_bias alphahatbetahat_bias taudothat_mse betahat_mse alphahat_mse #> 1 3.847844e-05 -0.0001897396 0.000676494 0.0006872088 0.0004818357 #> 2 2.483421e-04 0.0004790955 0.001316415 0.0013427927 0.0009874974 #> 3 7.425330e-04 0.0006458803 0.002766365 0.0027620920 0.0019795370 #> 4 -3.725460e-05 -0.0003117185 0.003403443 0.0033466278 0.0024626297 #> 5 -4.729428e-04 0.0004041179 0.004509607 0.0044726848 0.0033327492 #> 6 -1.375181e-03 -0.0004622805 0.007069120 0.0068445863 0.0050017626 #> alphahatbetahat_mse taudothat_rmse betahat_rmse alphahat_rmse #> 1 0.000588616 0.02600950 0.02621467 0.02195076 #> 2 0.001198437 0.03628243 0.03664414 0.03142447 #> 3 0.002434153 0.05259624 0.05255561 0.04449199 #> 4 0.002998572 0.05833904 0.05785005 0.04962489 #> 5 0.003947720 0.06715361 0.06687813 0.05772997 #> 6 0.006102248 0.08407806 0.08273202 0.07072314 #> alphahatbetahat_rmse missing std Method n_label alpha_label #> 1 0.02426141 Complete Unstandardized fit.sem n: 1000 α: 0.71 #> 2 0.03461845 Complete Unstandardized fit.sem n: 500 α: 0.71 #> 3 0.04933713 Complete Unstandardized fit.sem n: 250 α: 0.71 #> 4 0.05475922 Complete Unstandardized fit.sem n: 200 α: 0.71 #> 5 0.06283088 Complete Unstandardized fit.sem n: 150 α: 0.71 #> 6 0.07811689 Complete Unstandardized fit.sem n: 100 α: 0.71 #> beta_label taudot_label theta_label #> 1 β: 0.71 τ̇: 0.14 0.51(0.71,0.71) #> 2 β: 0.71 τ̇: 0.14 0.51(0.71,0.71) #> 3 β: 0.71 τ̇: 0.14 0.51(0.71,0.71) #> 4 β: 0.71 τ̇: 0.14 0.51(0.71,0.71) #> 5 β: 0.71 τ̇: 0.14 0.51(0.71,0.71) #> 6 β: 0.71 τ̇: 0.14 0.51(0.71,0.71)#> 'data.frame': 531 obs. of 61 variables: #> $ taskid : num 1 2 3 4 5 6 7 8 9 10 ... #> $ n : num 1000 500 250 200 150 100 75 50 20 1000 ... #> $ reps : num 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 ... #> $ taudot : num 0.141 0.141 0.141 0.141 0.141 ... #> $ beta : num 0.714 0.714 0.714 0.714 0.714 ... #> $ alpha : num 0.714 0.714 0.714 0.714 0.714 ... #> $ alphabeta : num 0.51 0.51 0.51 0.51 0.51 ... #> $ sigma2x : num 225 225 225 225 225 225 225 225 225 225 ... #> $ sigma2epsilonm : num 110 110 110 110 110 ... #> $ sigma2epsilony : num 73.3 73.3 73.3 73.3 73.3 ... #> $ mux : num 100 100 100 100 100 100 100 100 100 100 ... #> $ deltam : num 28.6 28.6 28.6 28.6 28.6 ... #> $ deltay : num 14.5 14.5 14.5 14.5 14.5 ... #> $ taudothat : num 0.141 0.141 0.141 0.141 0.14 ... #> $ betahat : num 0.714 0.714 0.714 0.714 0.715 ... #> $ alphahat : num 0.714 0.714 0.715 0.714 0.714 ... #> $ sigma2hatepsilonyhat : num 73.1 73 72.8 72.7 72.5 ... #> $ sigma2hatepsilonmhat : num 110 110 110 110 110 ... #> $ alphahatbetahat : num 0.51 0.51 0.511 0.51 0.51 ... #> $ sehattaudothat : num 0.0258 0.0365 0.0517 0.0579 0.0668 ... #> $ sehatbetahat : num 0.0258 0.0365 0.0517 0.0579 0.0668 ... #> $ sehatalphahat : num 0.0221 0.0314 0.0444 0.0496 0.0574 ... #> $ sehatsigma2hatepsilonyhat: num 3.27 4.62 6.52 7.29 8.4 ... #> $ sehatsigma2hatepsilonmhat: num 4.93 6.97 9.85 10.99 12.72 ... #> $ theta : num 0.51 0.51 0.511 0.51 0.51 ... #> $ taudothat_var : num 0.000677 0.001317 0.002766 0.003404 0.004509 ... #> $ betahat_var : num 0.000687 0.001343 0.002763 0.003347 0.004472 ... #> $ alphahat_var : num 0.000482 0.000988 0.001979 0.002463 0.003333 ... #> $ alphahatbetahat_var : num 0.000589 0.001198 0.002434 0.002999 0.003948 ... #> $ taudothat_sd : num 0.026 0.0363 0.0526 0.0583 0.0672 ... #> $ betahat_sd : num 0.0262 0.0366 0.0526 0.0579 0.0669 ... #> $ alphahat_sd : num 0.022 0.0314 0.0445 0.0496 0.0577 ... #> $ alphahatbetahat_sd : num 0.0243 0.0346 0.0493 0.0548 0.0628 ... #> $ taudothat_skew : num -0.0788 -0.0104 -0.0473 0.021 -0.0342 ... #> $ betahat_skew : num 0.00908 0.01005 0.04952 -0.03416 0.03879 ... #> $ alphahat_skew : num -0.0133 0.0022 -0.0527 0.0165 0.0298 ... #> $ alphahatbetahat_skew : num 0.109 0.119 0.161 0.121 0.185 ... #> $ taudothat_kurt : num -0.05923 -0.04607 0.03286 0.02098 0.00866 ... #> $ betahat_kurt : num -0.0711 0.0477 -0.1179 -0.1278 -0.0597 ... #> $ alphahat_kurt : num -0.06301 0.00885 -0.03949 -0.06564 0.17385 ... #> $ alphahatbetahat_kurt : num -0.02051 0.12769 -0.07217 0.00153 -0.07067 ... #> $ taudothat_bias : num -2.12e-05 -2.00e-04 -8.86e-04 -5.70e-06 -1.13e-03 ... #> $ betahat_bias : num -0.000293 0.000413 0.000155 -0.000449 0.001103 ... #> $ alphahat_bias : num 3.85e-05 2.48e-04 7.43e-04 -3.73e-05 -4.73e-04 ... #> $ alphahatbetahat_bias : num -0.00019 0.000479 0.000646 -0.000312 0.000404 ... #> $ taudothat_mse : num 0.000676 0.001316 0.002766 0.003403 0.00451 ... #> $ betahat_mse : num 0.000687 0.001343 0.002762 0.003347 0.004473 ... #> $ alphahat_mse : num 0.000482 0.000987 0.00198 0.002463 0.003333 ... #> $ alphahatbetahat_mse : num 0.000589 0.001198 0.002434 0.002999 0.003948 ... #> $ taudothat_rmse : num 0.026 0.0363 0.0526 0.0583 0.0672 ... #> $ betahat_rmse : num 0.0262 0.0366 0.0526 0.0579 0.0669 ... #> $ alphahat_rmse : num 0.022 0.0314 0.0445 0.0496 0.0577 ... #> $ alphahatbetahat_rmse : num 0.0243 0.0346 0.0493 0.0548 0.0628 ... #> $ missing : chr "Complete" "Complete" "Complete" "Complete" ... #> $ std : chr "Unstandardized" "Unstandardized" "Unstandardized" "Unstandardized" ... #> $ Method : chr "fit.sem" "fit.sem" "fit.sem" "fit.sem" ... #> $ n_label : Factor w/ 9 levels "n: 20","n: 50",..: 9 8 7 6 5 4 3 2 1 9 ... #> $ alpha_label : Factor w/ 4 levels "α: 0.00","α: 0.38",..: 4 4 4 4 4 4 4 4 4 4 ... #> $ beta_label : Factor w/ 4 levels "β: 0.00","β: 0.38",..: 4 4 4 4 4 4 4 4 4 4 ... #> $ taudot_label : Factor w/ 4 levels "τ̇: 0.00","τ̇: 0.14",..: 2 2 2 2 2 2 2 2 2 1 ... #> $ theta_label : chr "0.51(0.71,0.71)" "0.51(0.71,0.71)" "0.51(0.71,0.71)" "0.51(0.71,0.71)" ...