results_mvn_std_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 - Standardized - Complete Data - Fit Structural Equation Modeling

Usage

results_mvn_std_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).

lambdaxhat

Mean of estimated factor loading xlatent ~ x ≤ft( λ_x \right). Numerically equivalent to the standard deviation of x.

lambdamhat

Mean of estimated factor loading mlatent ~ m ≤ft( λ_m \right). Numerically equivalent to the standard deviation of m.

lambdayhat

Mean of estimated factor loading ylatent ~ y ≤ft( λ_y \right). Numerically equivalent to the standard deviation of y.

taudothatprime

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

betahatprime

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

alphahatprime

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

sigma2hatepsilonylatenthat

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

sigma2hatepsilonmlatenthat

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

alphahatprimebetahatprime

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

sehatlambdaxhat

Mean of estimated standard error of estimated factor loading xlatent ~ x ≤ft( λ_x \right).

sehatlambdamhat

Mean of estimated standard error of estimated factor loading mlatent ~ m ≤ft( λ_m \right).

sehatlambdayhat

Mean of estimated standard error of estimated factor loading ylatent ~ y ≤ft( λ_y \right).

sehattaudothatprime

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

sehatbetahatprime

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

sehatalphahatprime

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

sehatsigma2hatepsilonylatenthat

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

sehatsigma2hatepsilonmlatenthat

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

theta

Population parameter α^{\prime} β^{\prime}.

taudothatprime_var

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

betahatprime_var

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

alphahatprime_var

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

alphahatprimebetahatprime_var

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

taudothatprime_sd

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

betahatprime_sd

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

alphahatprime_sd

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

alphahatprimebetahatprime_sd

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

taudothatprime_skew

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

betahatprime_skew

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

alphahatprime_skew

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

alphahatprimebetahatprime_skew

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

taudothatprime_kurt

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

betahatprime_kurt

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

alphahatprime_kurt

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

alphahatprimebetahatprime_kurt

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

taudothatprime_bias

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

betahatprime_bias

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

alphahatprime_bias

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

alphahatprimebetahatprime_bias

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

taudothatprime_mse

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

betahatprime_mse

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

alphahatprime_mse

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

alphahatprimebetahatprime_mse

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

taudothatprime_rmse

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

betahatprime_rmse

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

alphahatprime_rmse

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

alphahatprimebetahatprime_rmse

Root mean square error of estimated standardized indirect effect of x on y through m ≤ft( \hat{α}^{\prime} \hat{β}^{\prime} \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 standardized simple mediation model is given by the following measurement model and regression model.

Measurement model

x_{\mathrm{latent}} = λ_x x

m_{\mathrm{latent}} = λ_m m

y_{\mathrm{latent}} = λ_y y

Regression model

y_{\mathrm{latent}} = \dot{τ}^{\prime} x_{\mathrm{latent}} + β^{\prime} m_{\mathrm{latent}} + \varepsilon_{y_{\mathrm{latent}}}

m_{\mathrm{latent}} = α^{\prime} x_{\mathrm{latent}} + \varepsilon_{m_{\mathrm{latent}}}

• The measurement errors in the measurement model are fixed to 0

• The variance of x_{\mathrm{latent}} ≤ft( σ_{x_{\mathrm{latent}}}^{2} \right) is fixed to 1

• The error variance \varepsilon_{y_{\mathrm{latent}}} ≤ft( σ_{\varepsilon_{y_{\mathrm{latent}}}}^{2} \right) is constrained to 1 - \dot{τ}^{\prime 2} - β^{\prime 2} - 2 \dot{τ}^{\prime} β^{\prime} α^{\prime}

• The error variance \varepsilon_{m_{\mathrm{latent}}} ≤ft( σ_{\varepsilon_{m_{\mathrm{latent}}}}^{2} \right) is constrained to 1 - α^{\prime 2}

See Also

Other results functions: results_mvn_std_mc.mvn.delta_ci, results_mvn_std_mc.mvn.sem_ci, results_mvn_std_mc.mvn.tb_ci, results_mvn_std_mc.wishart_ci, results_mvn_std_nb_ci, results_mvn_std_pb.mvn_ci

Examples

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

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