R/results_vm_sev_fit.sem.mlr.R
results_vm_sev_fit.sem.mlr.RdResults: Simple Mediation Model - Vale and Maurelli (1983) - Skewness = 3, Kurtosis = 21 - Complete Data - Fit Structural Equation Modeling with Robust Standard Errors
results_vm_sev_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_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_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_sev_fit.sem.mlr, package = "jeksterslabRmedsimple") head(results_vm_sev_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 0.1405775 0.7168743 #> 2 110.2721 73.3221 100 28.59258 14.45045 0.1412296 0.7178149 #> 3 110.2721 73.3221 100 28.59258 14.45045 0.1430123 0.7213405 #> 4 110.2721 73.3221 100 28.59258 14.45045 0.1424114 0.7205544 #> 5 110.2721 73.3221 100 28.59258 14.45045 0.1436878 0.7197689 #> 6 110.2721 73.3221 100 28.59258 14.45045 0.1488855 0.7232058 #> alphahat sigma2hatepsilonyhat sigma2hatepsilonmhat alphahatbetahat #> 1 0.7171620 72.47189 109.4344 0.5136024 #> 2 0.7184428 71.22271 109.1971 0.5146523 #> 3 0.7228395 70.05237 105.8953 0.5197992 #> 4 0.7233377 68.82235 106.0341 0.5188273 #> 5 0.7203816 67.57371 105.0165 0.5154481 #> 6 0.7251151 65.99402 101.5859 0.5195372 #> sehattaudothat sehatbetahat sehatalphahat sehatsigma2hatepsilonyhat #> 1 0.05075152 0.06212445 0.05632695 9.642783 #> 2 0.06760017 0.08237327 0.07549954 12.572829 #> 3 0.08829062 0.10659288 0.09620765 16.125616 #> 4 0.09577793 0.11438109 0.10452083 17.037799 #> 5 0.10444805 0.12621917 0.11457460 18.346306 #> 6 0.12068988 0.14196777 0.12796576 20.364659 #> sehatsigma2hatepsilonmhat theta taudothat_var betahat_var alphahat_var #> 1 14.85108 0.5136024 0.002943791 0.004533589 0.003721314 #> 2 19.98286 0.5146523 0.005894046 0.008692189 0.007333391 #> 3 25.05329 0.5197992 0.010956766 0.016013853 0.013483498 #> 4 27.43496 0.5188273 0.013388740 0.019743520 0.016121881 #> 5 29.64414 0.5154481 0.017026663 0.026408908 0.020591418 #> 6 32.93293 0.5195372 0.025111602 0.036602666 0.027956423 #> alphahatbetahat_var taudothat_sd betahat_sd alphahat_sd alphahatbetahat_sd #> 1 0.003750998 0.05425671 0.06733193 0.06100258 0.06124539 #> 2 0.007328447 0.07677269 0.09323191 0.08563522 0.08560635 #> 3 0.014052873 0.10467457 0.12654585 0.11611847 0.11854481 #> 4 0.016590642 0.11570972 0.14051164 0.12697197 0.12880466 #> 5 0.021698081 0.13048626 0.16250818 0.14349710 0.14730268 #> 6 0.030010733 0.15846641 0.19131823 0.16720174 0.17323606 #> taudothat_skew betahat_skew alphahat_skew alphahatbetahat_skew taudothat_kurt #> 1 0.1297877 0.1116628 0.1735488 0.3766715 0.2694572 #> 2 0.2657142 0.2061105 0.3417611 0.6091326 0.7007134 #> 3 0.2129594 0.3125085 0.3284657 0.6953482 0.7666429 #> 4 0.1376827 0.3807143 0.5073356 0.7908229 0.4931908 #> 5 0.1925373 0.3813093 0.5605250 0.8010945 0.8654084 #> 6 0.3154227 0.4391170 0.6166729 1.0863591 1.0710805 #> betahat_kurt alphahat_kurt alphahatbetahat_kurt taudothat_bias betahat_bias #> 1 0.1703560 0.2568154 0.6802524 -0.0008438973 0.002800074 #> 2 0.2850748 0.4423660 1.0976503 -0.0001917084 0.003740706 #> 3 0.4016593 0.2444437 1.0625561 0.0015909197 0.007266316 #> 4 0.6272913 0.8158343 1.5508913 0.0009900337 0.006480219 #> 5 0.3510662 0.7874043 1.1657592 0.0022664693 0.005694694 #> 6 0.8298258 1.0798116 3.9921099 0.0074641876 0.009131608 #> alphahat_bias alphahatbetahat_bias taudothat_mse betahat_mse alphahat_mse #> 1 0.003087840 0.003700464 0.002943914 0.004540523 0.003730105 #> 2 0.004368656 0.004750396 0.005892904 0.008704444 0.007351009 #> 3 0.008765293 0.009897239 0.010957105 0.016063450 0.013557632 #> 4 0.009263516 0.008925383 0.013387043 0.019781565 0.016204470 #> 5 0.006307400 0.005546099 0.017028395 0.026436056 0.020627083 #> 6 0.011040871 0.009635283 0.025162294 0.036678731 0.028072732 #> alphahatbetahat_mse taudothat_rmse betahat_rmse alphahat_rmse #> 1 0.003763941 0.05425785 0.06738340 0.06107458 #> 2 0.007349547 0.07676525 0.09329761 0.08573803 #> 3 0.014148018 0.10467619 0.12674167 0.11643724 #> 4 0.016666986 0.11570239 0.14064695 0.12729678 #> 5 0.021724500 0.13049289 0.16259168 0.14362132 #> 6 0.030097570 0.15862627 0.19151692 0.16754919 #> alphahatbetahat_rmse missing std Method n_label alpha_label #> 1 0.06135097 Complete Unstandardized fit.sem.mlr n: 1000 α: 0.71 #> 2 0.08572950 Complete Unstandardized fit.sem.mlr n: 500 α: 0.71 #> 3 0.11894544 Complete Unstandardized fit.sem.mlr n: 250 α: 0.71 #> 4 0.12910068 Complete Unstandardized fit.sem.mlr n: 200 α: 0.71 #> 5 0.14739233 Complete Unstandardized fit.sem.mlr n: 150 α: 0.71 #> 6 0.17348651 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 0.52(0.71,0.71) #> 4 β: 0.71 τ̇: 0.14 0.52(0.71,0.71) #> 5 β: 0.71 τ̇: 0.14 0.52(0.71,0.71) #> 6 β: 0.71 τ̇: 0.14 0.52(0.71,0.71)#> 'data.frame': 522 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 0.143 0.142 0.144 ... #> $ betahat : num 0.717 0.718 0.721 0.721 0.72 ... #> $ alphahat : num 0.717 0.718 0.723 0.723 0.72 ... #> $ sigma2hatepsilonyhat : num 72.5 71.2 70.1 68.8 67.6 ... #> $ sigma2hatepsilonmhat : num 109 109 106 106 105 ... #> $ alphahatbetahat : num 0.514 0.515 0.52 0.519 0.515 ... #> $ sehattaudothat : num 0.0508 0.0676 0.0883 0.0958 0.1044 ... #> $ sehatbetahat : num 0.0621 0.0824 0.1066 0.1144 0.1262 ... #> $ sehatalphahat : num 0.0563 0.0755 0.0962 0.1045 0.1146 ... #> $ sehatsigma2hatepsilonyhat: num 9.64 12.57 16.13 17.04 18.35 ... #> $ sehatsigma2hatepsilonmhat: num 14.9 20 25.1 27.4 29.6 ... #> $ theta : num 0.514 0.515 0.52 0.519 0.515 ... #> $ taudothat_var : num 0.00294 0.00589 0.01096 0.01339 0.01703 ... #> $ betahat_var : num 0.00453 0.00869 0.01601 0.01974 0.02641 ... #> $ alphahat_var : num 0.00372 0.00733 0.01348 0.01612 0.02059 ... #> $ alphahatbetahat_var : num 0.00375 0.00733 0.01405 0.01659 0.0217 ... #> $ taudothat_sd : num 0.0543 0.0768 0.1047 0.1157 0.1305 ... #> $ betahat_sd : num 0.0673 0.0932 0.1265 0.1405 0.1625 ... #> $ alphahat_sd : num 0.061 0.0856 0.1161 0.127 0.1435 ... #> $ alphahatbetahat_sd : num 0.0612 0.0856 0.1185 0.1288 0.1473 ... #> $ taudothat_skew : num 0.13 0.266 0.213 0.138 0.193 ... #> $ betahat_skew : num 0.112 0.206 0.313 0.381 0.381 ... #> $ alphahat_skew : num 0.174 0.342 0.328 0.507 0.561 ... #> $ alphahatbetahat_skew : num 0.377 0.609 0.695 0.791 0.801 ... #> $ taudothat_kurt : num 0.269 0.701 0.767 0.493 0.865 ... #> $ betahat_kurt : num 0.17 0.285 0.402 0.627 0.351 ... #> $ alphahat_kurt : num 0.257 0.442 0.244 0.816 0.787 ... #> $ alphahatbetahat_kurt : num 0.68 1.1 1.06 1.55 1.17 ... #> $ taudothat_bias : num -0.000844 -0.000192 0.001591 0.00099 0.002266 ... #> $ betahat_bias : num 0.0028 0.00374 0.00727 0.00648 0.00569 ... #> $ alphahat_bias : num 0.00309 0.00437 0.00877 0.00926 0.00631 ... #> $ alphahatbetahat_bias : num 0.0037 0.00475 0.0099 0.00893 0.00555 ... #> $ taudothat_mse : num 0.00294 0.00589 0.01096 0.01339 0.01703 ... #> $ betahat_mse : num 0.00454 0.0087 0.01606 0.01978 0.02644 ... #> $ alphahat_mse : num 0.00373 0.00735 0.01356 0.0162 0.02063 ... #> $ alphahatbetahat_mse : num 0.00376 0.00735 0.01415 0.01667 0.02172 ... #> $ taudothat_rmse : num 0.0543 0.0768 0.1047 0.1157 0.1305 ... #> $ betahat_rmse : num 0.0674 0.0933 0.1267 0.1406 0.1626 ... #> $ alphahat_rmse : num 0.0611 0.0857 0.1164 0.1273 0.1436 ... #> $ alphahatbetahat_rmse : num 0.0614 0.0857 0.1189 0.1291 0.1474 ... #> $ 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)" "0.52(0.71,0.71)" "0.52(0.71,0.71)" ...