results_mvn_mcar_fit.sem: Results: Simple Mediation Model - Multivariate Normal...

In jeksterslabds/jeksterslabRmedsimple: Jekster's Lab Simple Mediation Model Utilities

Description

Results: Simple Mediation Model - Multivariate Normal Distribution - Data Missing Completely at Random - Fit Structural Equation Modeling with Full Information Maximum Likelihood

Usage

results_mvn_mcar_fit.sem

Format

A data frame with the following variables

taskid

Simulation task identification number.

n

Sample size.

reps

Monte Carlo replications.

taudot

Population slope of path from x to y ≤ft( \dot{τ} \right).

beta

Population slope of path from m to y ≤ft( β \right).

alpha

Population slope of path from x to m ≤ft( α \right).

alphabeta

Population indirect effect of x on y through m ≤ft( α β \right).

sigma2x

Population variance of x ≤ft( σ_{x}^{2} \right).

sigma2epsilonm

Population error variance of m ≤ft( σ_{\varepsilon_{m}}^{2} \right).

sigma2epsilony

Population error variance of y ≤ft( σ_{\varepsilon_{y}}^{2} \right).

mux

Population mean of x ≤ft( μ_x \right).

deltam

Population intercept of m ≤ft( δ_m \right).

deltay

Population intercept of y ≤ft( δ_y \right).

taudothat

Mean of estimated slope of path from x to y ≤ft( \hat{\dot{τ}} \right).

betahat

Mean of estimated slope of path from m to y ≤ft( \hat{β} \right).

alphahat

Mean of estimated slope of path from x to m ≤ft( \hat{α} \right).

sigma2hatepsilonyhat

Mean of estimated error variance of y ≤ft( \hat{σ}_{\varepsilon_{y}}^{2} \right).

sigma2hatepsilonmhat

Mean of estimated error variance of m ≤ft( \hat{σ}_{\varepsilon_{m}}^{2} \right).

deltayhat

Mean of estimated intercept of y ≤ft( \hat{δ}_y \right).

deltamhat

Mean of estimated intercept of m ≤ft( \hat{δ}_{m} \right).

muxhat

Mean of estimated mean of x ≤ft( \hat{μ}_x \right).

sigma2xhat

Mean of estimated variance of x ≤ft( \hat{σ}_{x}^{2} \right).

alphahatbetahat

Mean of estimated indirect effect of x on y through m ≤ft( \hat{α} \hat{β} \right).

sehattaudothat

Mean of estimated standard error of \hat{\dot{τ}}.

sehatbetahat

Mean of estimated standard error of \hat{β}.

sehatalphahat

Mean of estimated standard error of \hat{α}.

sehatsigma2hatepsilonyhat

Mean of estimated standard error of error variance of y ≤ft( \hat{σ}_{\varepsilon_{y}}^{2} \right).

sehatsigma2hatepsilonmhat

Mean of estimated standard error of error variance of m ≤ft( \hat{σ}_{\varepsilon_{m}}^{2} \right).

sehatdeltayhat

Mean of estimated standard error of \hat{δ}_{y}.

sehatdeltamhat

Mean of estimated standard error of \hat{δ}_{m}.

sehatmuxhat

Mean of estimated standard error of mean of x ≤ft( \hat{μ}_x \right).

sehatsigma2xhat

Mean of estimated standard error of variance of x ≤ft( \hat{σ}_{x}^{2} \right).

theta

Population parameter α β.

taudothat_var

Variance of estimated slope of path from x to y ≤ft( \hat{\dot{τ}} \right).

betahat_var

Variance of estimated slope of path from m to y ≤ft( \hat{β} \right).

alphahat_var

Variance of estimated slope of path from x to m ≤ft( \hat{α} \right).

alphahatbetahat_var

Variance of estimated indirect effect of x on y through m ≤ft( \hat{α} \hat{β} \right).

taudothat_sd

Standard deviation of estimated slope of path from x to y ≤ft( \hat{\dot{τ}} \right).

betahat_sd

Standard deviation of estimated slope of path from m to y ≤ft( \hat{β} \right).

alphahat_sd

Standard deviation of estimated slope of path from x to m ≤ft( \hat{α} \right).

alphahatbetahat_sd

Standard deviation of estimated indirect effect of x on y through m ≤ft( \hat{α} \hat{β} \right).

taudothat_skew

Skewness of estimated slope of path from x to y ≤ft( \hat{\dot{τ}} \right).

betahat_skew

Skewness of estimated slope of path from m to y ≤ft( \hat{β} \right).

alphahat_skew

Skewness of estimated slope of path from x to m ≤ft( \hat{α} \right).

alphahatbetahat_skew

Skewness of estimated indirect effect of x on y through m ≤ft( \hat{α} \hat{β} \right).

taudothat_kurt

Excess kurtosis of estimated slope of path from x to y ≤ft( \hat{\dot{τ}} \right).

betahat_kurt

Excess kurtosis of estimated slope of path from m to y ≤ft( \hat{β} \right).

alphahat_kurt

Excess kurtosis of estimated slope of path from x to m ≤ft( \hat{α} \right).

alphahatbetahat_kurt

Excess kurtosis of estimated indirect effect of x on y through m ≤ft( \hat{α} \hat{β} \right).

taudothat_bias

Bias of estimated slope of path from x to y ≤ft( \hat{\dot{τ}} \right).

betahat_bias

Bias of estimated slope of path from m to y ≤ft( \hat{β} \right).

alphahat_bias

Bias of estimated slope of path from x to m ≤ft( \hat{α} \right).

alphahatbetahat_bias

Bias of estimated indirect effect of x on y through m ≤ft( \hat{α} \hat{β} \right).

taudothat_mse

Mean square error of estimated slope of path from x to y ≤ft( \hat{\dot{τ}} \right).

betahat_mse

Mean square error of estimated slope of path from m to y ≤ft( \hat{β} \right).

alphahat_mse

Mean square error of estimated slope of path from x to m ≤ft( \hat{α} \right).

alphahatbetahat_mse

Mean square error of estimated indirect effect of x on y through m ≤ft( \hat{α} \hat{β} \right).

taudothat_rmse

Root mean square error of estimated slope of path from x to y ≤ft( \hat{\dot{τ}} \right).

betahat_rmse

Root mean square error of estimated slope of path from m to y ≤ft( \hat{β} \right).

alphahat_rmse

Root mean square error of estimated slope of path from x to m ≤ft( \hat{α} \right).

alphahatbetahat_rmse

Root mean square error of estimated indirect effect of x on y through m ≤ft( \hat{α} \hat{β} \right).

missing

Type of missingness.

std

Standardized vs. unstandardize indirect effect.

Method

Method used. Fit in this case.

n_label

Sample size labels.

alpha_label

α labels.

beta_label

β labels.

taudot_label

\dot{τ} labels.

theta_label

θ labels.

Details

The simple mediation model is given by

y_i = δ_y + \dot{τ} x_i + β m_i + \varepsilon_{y_{i}}

m_i = δ_m + α x_i + \varepsilon_{m_{i}}

The parameters for the mean structure are

\boldsymbol{θ}_{\text{mean structure}} = ≤ft\{ μ_x, δ_m, δ_y \right\} .

The parameters for the covariance structure are

\boldsymbol{θ}_{\text{covariance structure}} = ≤ft\{ \dot{τ}, β, α, σ_{x}^{2}, σ_{\varepsilon_{m}}^{2}, σ_{\varepsilon_{y}}^{2} \right\} .

See Also

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_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

Examples

data(results_mvn_mcar_fit.sem, package = "jeksterslabRmedsimple")
head(results_mvn_mcar_fit.sem)
str(results_mvn_mcar_fit.sem)

jeksterslabds/jeksterslabRmedsimple documentation built on Oct. 16, 2020, 11:30 a.m.