R/results_vm_mod_fit.sem.mlr.R
results_vm_mod_fit.sem.mlr.RdResults: Simple Mediation Model - Vale and Maurelli (1983) - Skewness = 2, Kurtosis = 7 - Complete Data - Fit Structural Equation Modeling with Robust Standard Errors
results_vm_mod_fit.sem.mlr
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_fit.sem,
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_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
data(results_vm_mod_fit.sem.mlr, package = "jeksterslabRmedsimple") head(results_vm_mod_fit.sem.mlr)#> 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 0.1419724 0.7135317 #> 2 110.2721 73.3221 100 28.59258 14.45045 0.1417535 0.7149333 #> 3 110.2721 73.3221 100 28.59258 14.45045 0.1411131 0.7169005 #> 4 110.2721 73.3221 100 28.59258 14.45045 0.1414830 0.7166915 #> 5 110.2721 73.3221 100 28.59258 14.45045 0.1407554 0.7159442 #> 6 110.2721 73.3221 100 28.59258 14.45045 0.1443344 0.7182118 #> alphahat sigma2hatepsilonyhat sigma2hatepsilonmhat alphahatbetahat #> 1 0.7151324 72.80576 110.1457 0.5100761 #> 2 0.7164727 72.81377 110.0791 0.5118519 #> 3 0.7152514 71.44650 109.0843 0.5120330 #> 4 0.7180198 71.15136 108.5212 0.5138135 #> 5 0.7192290 69.68427 107.7647 0.5142247 #> 6 0.7226573 69.37700 107.1072 0.5175581 #> sehattaudothat sehatbetahat sehatalphahat sehatsigma2hatepsilonyhat #> 1 0.03744472 0.04316766 0.03868579 6.380195 #> 2 0.05184811 0.05932681 0.05335000 8.767208 #> 3 0.06996704 0.07958387 0.07161428 11.548100 #> 4 0.07733728 0.08701027 0.07852889 12.622862 #> 5 0.08675137 0.09651933 0.08799498 13.796660 #> 6 0.10269685 0.11389025 0.10250131 15.950300 #> sehatsigma2hatepsilonmhat theta taudothat_var betahat_var alphahat_var #> 1 9.813975 0.5100761 0.001450647 0.001979622 0.001588460 #> 2 13.511185 0.5118519 0.003008059 0.003931904 0.003169325 #> 3 18.139995 0.5120330 0.005831230 0.007656005 0.006320279 #> 4 19.740265 0.5138135 0.007209828 0.009448197 0.007725923 #> 5 22.115501 0.5142247 0.009184364 0.012037354 0.009837431 #> 6 25.600160 0.5175581 0.014349527 0.018798980 0.015129464 #> alphahatbetahat_var taudothat_sd betahat_sd alphahat_sd alphahatbetahat_sd #> 1 0.001629514 0.03808736 0.04449294 0.03985549 0.04036724 #> 2 0.003278402 0.05484577 0.06270489 0.05629676 0.05725733 #> 3 0.006456290 0.07636249 0.08749860 0.07950018 0.08035104 #> 4 0.008176040 0.08491070 0.09720184 0.08789723 0.09042146 #> 5 0.010775171 0.09583509 0.10971488 0.09918382 0.10380352 #> 6 0.016441848 0.11978951 0.13710937 0.12300189 0.12822577 #> taudothat_skew betahat_skew alphahat_skew alphahatbetahat_skew taudothat_kurt #> 1 0.04526031 0.08062369 0.1288351 0.2285358 0.1645779 #> 2 0.06625113 0.10862226 0.1693780 0.3516740 0.0543966 #> 3 0.14131299 0.19750169 0.2298648 0.3960709 0.3053539 #> 4 0.10759488 0.15994336 0.2383293 0.5003035 0.1515612 #> 5 0.13667266 0.18376025 0.2581478 0.5610875 0.1112502 #> 6 0.06394001 0.33294036 0.4335725 0.6935200 0.3093689 #> betahat_kurt alphahat_kurt alphahatbetahat_kurt taudothat_bias betahat_bias #> 1 0.03800453 0.01345412 0.008517339 5.510394e-04 -0.0005425166 #> 2 0.05200629 0.12952769 0.376566024 3.321876e-04 0.0008590953 #> 3 0.26718692 0.24462744 0.334225791 -3.082811e-04 0.0028263140 #> 4 0.25545229 0.21267827 0.595230061 6.163878e-05 0.0026173412 #> 5 0.11922658 0.13855946 0.659167421 -6.659869e-04 0.0018699631 #> 6 0.42265207 0.55551048 1.088106454 2.913023e-03 0.0041376470 #> alphahat_bias alphahatbetahat_bias taudothat_mse betahat_mse alphahat_mse #> 1 0.001058213 0.0001741847 0.001450660 0.001979520 0.001589262 #> 2 0.002398469 0.0019499348 0.003007568 0.003931855 0.003174444 #> 3 0.001177233 0.0021310314 0.005830159 0.007662462 0.006320401 #> 4 0.003945640 0.0039115912 0.007208390 0.009453158 0.007739946 #> 5 0.005154855 0.0043227877 0.009182971 0.012038444 0.009862036 #> 6 0.008583132 0.0076561877 0.014355143 0.018812340 0.015200109 #> alphahatbetahat_mse taudothat_rmse betahat_rmse alphahat_rmse #> 1 0.001629219 0.03808754 0.04449180 0.03986555 #> 2 0.003281549 0.05484130 0.06270451 0.05634220 #> 3 0.006459540 0.07635548 0.08753549 0.07950095 #> 4 0.008189705 0.08490224 0.09722735 0.08797696 #> 5 0.010791703 0.09582782 0.10971984 0.09930778 #> 6 0.016497177 0.11981295 0.13715809 0.12328872 #> alphahatbetahat_rmse missing std Method n_label alpha_label #> 1 0.04036358 Complete Unstandardized fit.sem.mlr n: 1000 α: 0.71 #> 2 0.05728480 Complete Unstandardized fit.sem.mlr n: 500 α: 0.71 #> 3 0.08037126 Complete Unstandardized fit.sem.mlr n: 250 α: 0.71 #> 4 0.09049699 Complete Unstandardized fit.sem.mlr n: 200 α: 0.71 #> 5 0.10388312 Complete Unstandardized fit.sem.mlr n: 150 α: 0.71 #> 6 0.12844134 Complete Unstandardized fit.sem.mlr 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 0.52(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 0.142 0.142 0.141 0.141 0.141 ... #> $ betahat : num 0.714 0.715 0.717 0.717 0.716 ... #> $ alphahat : num 0.715 0.716 0.715 0.718 0.719 ... #> $ sigma2hatepsilonyhat : num 72.8 72.8 71.4 71.2 69.7 ... #> $ sigma2hatepsilonmhat : num 110 110 109 109 108 ... #> $ alphahatbetahat : num 0.51 0.512 0.512 0.514 0.514 ... #> $ sehattaudothat : num 0.0374 0.0518 0.07 0.0773 0.0868 ... #> $ sehatbetahat : num 0.0432 0.0593 0.0796 0.087 0.0965 ... #> $ sehatalphahat : num 0.0387 0.0534 0.0716 0.0785 0.088 ... #> $ sehatsigma2hatepsilonyhat: num 6.38 8.77 11.55 12.62 13.8 ... #> $ sehatsigma2hatepsilonmhat: num 9.81 13.51 18.14 19.74 22.12 ... #> $ theta : num 0.51 0.512 0.512 0.514 0.514 ... #> $ taudothat_var : num 0.00145 0.00301 0.00583 0.00721 0.00918 ... #> $ betahat_var : num 0.00198 0.00393 0.00766 0.00945 0.01204 ... #> $ alphahat_var : num 0.00159 0.00317 0.00632 0.00773 0.00984 ... #> $ alphahatbetahat_var : num 0.00163 0.00328 0.00646 0.00818 0.01078 ... #> $ taudothat_sd : num 0.0381 0.0548 0.0764 0.0849 0.0958 ... #> $ betahat_sd : num 0.0445 0.0627 0.0875 0.0972 0.1097 ... #> $ alphahat_sd : num 0.0399 0.0563 0.0795 0.0879 0.0992 ... #> $ alphahatbetahat_sd : num 0.0404 0.0573 0.0804 0.0904 0.1038 ... #> $ taudothat_skew : num 0.0453 0.0663 0.1413 0.1076 0.1367 ... #> $ betahat_skew : num 0.0806 0.1086 0.1975 0.1599 0.1838 ... #> $ alphahat_skew : num 0.129 0.169 0.23 0.238 0.258 ... #> $ alphahatbetahat_skew : num 0.229 0.352 0.396 0.5 0.561 ... #> $ taudothat_kurt : num 0.1646 0.0544 0.3054 0.1516 0.1113 ... #> $ betahat_kurt : num 0.038 0.052 0.267 0.255 0.119 ... #> $ alphahat_kurt : num 0.0135 0.1295 0.2446 0.2127 0.1386 ... #> $ alphahatbetahat_kurt : num 0.00852 0.37657 0.33423 0.59523 0.65917 ... #> $ taudothat_bias : num 5.51e-04 3.32e-04 -3.08e-04 6.16e-05 -6.66e-04 ... #> $ betahat_bias : num -0.000543 0.000859 0.002826 0.002617 0.00187 ... #> $ alphahat_bias : num 0.00106 0.0024 0.00118 0.00395 0.00515 ... #> $ alphahatbetahat_bias : num 0.000174 0.00195 0.002131 0.003912 0.004323 ... #> $ taudothat_mse : num 0.00145 0.00301 0.00583 0.00721 0.00918 ... #> $ betahat_mse : num 0.00198 0.00393 0.00766 0.00945 0.01204 ... #> $ alphahat_mse : num 0.00159 0.00317 0.00632 0.00774 0.00986 ... #> $ alphahatbetahat_mse : num 0.00163 0.00328 0.00646 0.00819 0.01079 ... #> $ taudothat_rmse : num 0.0381 0.0548 0.0764 0.0849 0.0958 ... #> $ betahat_rmse : num 0.0445 0.0627 0.0875 0.0972 0.1097 ... #> $ alphahat_rmse : num 0.0399 0.0563 0.0795 0.088 0.0993 ... #> $ alphahatbetahat_rmse : num 0.0404 0.0573 0.0804 0.0905 0.1039 ... #> $ missing : chr "Complete" "Complete" "Complete" "Complete" ... #> $ std : chr "Unstandardized" "Unstandardized" "Unstandardized" "Unstandardized" ... #> $ Method : chr "fit.sem.mlr" "fit.sem.mlr" "fit.sem.mlr" "fit.sem.mlr" ... #> $ 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)" ...