Fits covariances and means of \(x\), \(m\), and \(y\) using structural equation modeling.
fit.cov(data)
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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.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_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
#>#>#> temp thirst water #> temp 1.2934694 0.4379592 0.4661224 #> thirst 0.4379592 1.0779592 0.5771429 #> water 0.4661224 0.5771429 1.2881633#> temp thirst water #> 70.18 3.06 3.24#> lavaan 0.6-7 ended normally after 21 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 50 #> Number of missing patterns 1 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 0.429 0.176 2.434 0.015 #> y (sgmxy) 0.457 0.192 2.377 0.017 #> m ~~ #> y (sgmm) 0.566 0.184 3.079 0.002 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 3.240 0.161 20.186 0.000 #> m (mmht) 3.060 0.147 20.840 0.000 #> x (mxht) 70.180 0.161 436.335 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 1.268 0.256 4.950 0.000 #> m (sgm2m) 1.056 0.213 4.950 0.000 #> y (sgm2y) 1.262 0.255 4.950 0.000 #>taskid <- 1 data <- mvn_dat(taskid = taskid) # Complete Data ---------------------------------------------------- cov(data)#> x m y #> x 220.7221 156.5965 146.5410 #> m 156.5965 230.4484 186.6715 #> y 146.5410 186.6715 231.7092#> x m y #> 100.3094 100.8612 100.6218#> lavaan 0.6-7 ended normally after 58 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 1 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 156.440 8.678 18.027 0.000 #> y (sgmxy) 146.394 8.517 17.188 0.000 #> m ~~ #> y (sgmm) 186.485 9.389 19.862 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.622 0.481 209.036 0.000 #> m (mmht) 100.861 0.480 210.105 0.000 #> x (mxht) 100.309 0.470 213.510 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 220.501 9.866 22.349 0.000 #> m (sgm2m) 230.218 10.301 22.349 0.000 #> y (sgm2y) 231.478 10.357 22.349 0.000 #># Missing completely at random ------------------------------------- ## 10% missing summary(fit.cov(data = mvn_mcar_10_dat(data = data, taskid = taskid)))#> lavaan 0.6-7 ended normally after 82 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 156.252 8.769 17.819 0.000 #> y (sgmxy) 147.915 8.660 17.081 0.000 #> m ~~ #> y (sgmm) 185.495 9.413 19.707 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.608 0.485 207.470 0.000 #> m (mmht) 100.833 0.481 209.778 0.000 #> x (mxht) 100.359 0.475 211.298 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 221.842 10.058 22.057 0.000 #> m (sgm2m) 228.524 10.330 22.122 0.000 #> y (sgm2y) 231.857 10.468 22.149 0.000 #>#> lavaan 0.6-7 ended normally after 72 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 157.418 8.880 17.727 0.000 #> y (sgmxy) 145.649 8.726 16.691 0.000 #> m ~~ #> y (sgmm) 185.681 9.462 19.624 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.527 0.484 207.673 0.000 #> m (mmht) 100.983 0.486 207.935 0.000 #> x (mxht) 100.260 0.481 208.395 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 223.696 10.258 21.807 0.000 #> m (sgm2m) 230.567 10.507 21.944 0.000 #> y (sgm2y) 227.946 10.399 21.919 0.000 #>#> lavaan 0.6-7 ended normally after 73 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 156.544 8.930 17.531 0.000 #> y (sgmxy) 141.938 8.710 16.297 0.000 #> m ~~ #> y (sgmm) 185.215 9.523 19.449 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.499 0.487 206.310 0.000 #> m (mmht) 100.824 0.488 206.481 0.000 #> x (mxht) 100.402 0.483 207.952 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 219.758 10.280 21.377 0.000 #> m (sgm2m) 230.581 10.574 21.806 0.000 #> y (sgm2y) 228.058 10.535 21.647 0.000 #># Missing at random ------------------------------------------------ ## 10% missing summary(fit.cov(data = mvn_mar_10_dat(data = data, taskid = taskid)))#> lavaan 0.6-7 ended normally after 81 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 156.644 8.775 17.851 0.000 #> y (sgmxy) 144.449 8.585 16.825 0.000 #> m ~~ #> y (sgmm) 185.827 9.459 19.645 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.646 0.483 208.198 0.000 #> m (mmht) 100.884 0.484 208.620 0.000 #> x (mxht) 100.278 0.472 212.423 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 219.296 9.955 22.030 0.000 #> m (sgm2m) 230.970 10.473 22.054 0.000 #> y (sgm2y) 229.470 10.439 21.983 0.000 #>#> lavaan 0.6-7 ended normally after 74 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 153.801 8.764 17.548 0.000 #> y (sgmxy) 144.983 8.678 16.706 0.000 #> m ~~ #> y (sgmm) 186.642 9.540 19.564 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.674 0.489 205.905 0.000 #> m (mmht) 100.859 0.484 208.436 0.000 #> x (mxht) 100.178 0.474 211.237 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 217.094 10.016 21.674 0.000 #> m (sgm2m) 229.411 10.453 21.946 0.000 #> y (sgm2y) 232.797 10.675 21.807 0.000 #>#> lavaan 0.6-7 ended normally after 77 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 151.907 8.888 17.091 0.000 #> y (sgmxy) 141.511 8.702 16.263 0.000 #> m ~~ #> y (sgmm) 184.766 9.583 19.280 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.613 0.493 204.107 0.000 #> m (mmht) 100.802 0.488 206.403 0.000 #> x (mxht) 100.126 0.478 209.343 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 215.840 10.108 21.354 0.000 #> m (sgm2m) 229.056 10.668 21.472 0.000 #> y (sgm2y) 231.452 10.752 21.526 0.000 #># Missing Not at random -------------------------------------------- ## 10% missing summary(fit.cov(data = mvn_mnar_10_dat(data = data, taskid = taskid)))#> lavaan 0.6-7 ended normally after 76 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 154.472 8.646 17.865 0.000 #> y (sgmxy) 144.674 8.530 16.960 0.000 #> m ~~ #> y (sgmm) 184.438 9.366 19.693 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.494 0.482 208.540 0.000 #> m (mmht) 100.754 0.479 210.487 0.000 #> x (mxht) 100.249 0.471 212.910 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 218.540 9.895 22.086 0.000 #> m (sgm2m) 226.475 10.251 22.092 0.000 #> y (sgm2y) 230.329 10.405 22.137 0.000 #>#> lavaan 0.6-7 ended normally after 75 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 150.269 8.647 17.377 0.000 #> y (sgmxy) 140.136 8.538 16.413 0.000 #> m ~~ #> y (sgmm) 186.831 9.584 19.494 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.471 0.489 205.293 0.000 #> m (mmht) 100.689 0.486 207.360 0.000 #> x (mxht) 99.822 0.467 213.690 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 210.127 9.728 21.601 0.000 #> m (sgm2m) 230.698 10.539 21.890 0.000 #> y (sgm2y) 232.581 10.717 21.703 0.000 #>#> lavaan 0.6-7 ended normally after 75 iterations #> #> Estimator ML #> Optimization method NLMINB #> Number of free parameters 9 #> #> Number of observations 1000 #> Number of missing patterns 4 #> #> Model Test User Model: #> #> Test statistic 0.000 #> Degrees of freedom 0 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Observed #> Observed information based on Hessian #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> x ~~ #> m (sgmxm) 151.919 8.851 17.164 0.000 #> y (sgmxy) 141.145 8.671 16.277 0.000 #> m ~~ #> y (sgmm) 184.720 9.473 19.499 0.000 #> #> Intercepts: #> Estimate Std.Err z-value P(>|z|) #> y (myht) 100.349 0.486 206.344 0.000 #> m (mmht) 100.570 0.486 206.906 0.000 #> x (mxht) 99.960 0.481 207.991 0.000 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> x (sgm2x) 218.206 10.219 21.353 0.000 #> m (sgm2m) 226.985 10.542 21.532 0.000 #> y (sgm2y) 226.971 10.476 21.666 0.000 #>