Monte Carlo Simulation Parameters (Multivariate Normal)
paramsmvn
A data frame with 12 variables
Simulation task identification number.
Sample size.
Population mean of x
\(\left( \mu_x \right)\).
Population mean of m
\(\left( \mu_m \right)\).
Population mean of y
\(\left( \mu_y \right)\).
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 variance of x
\(\left( \sigma_{x}^{2} \right)\)
Population variance of m
\(\left( \sigma_{m}^{2} \right)\)
Population variance of y
\(\left( \sigma_{y}^{2} \right)\)
Monte Carlo replications.
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 parameters functions:
paramsbeta
,
paramsexp
#> taskid n mux mum muy taudot beta alpha sigma2x sigma2m sigma2y #> 1 1 1000 100 100 100 0.1414214 0.7140742 0.7140742 225 225 225 #> 2 2 500 100 100 100 0.1414214 0.7140742 0.7140742 225 225 225 #> 3 3 250 100 100 100 0.1414214 0.7140742 0.7140742 225 225 225 #> 4 4 200 100 100 100 0.1414214 0.7140742 0.7140742 225 225 225 #> 5 5 150 100 100 100 0.1414214 0.7140742 0.7140742 225 225 225 #> 6 6 100 100 100 100 0.1414214 0.7140742 0.7140742 225 225 225 #> reps #> 1 5000 #> 2 5000 #> 3 5000 #> 4 5000 #> 5 5000 #> 6 5000#> 'data.frame': 531 obs. of 12 variables: #> $ taskid : int 1 2 3 4 5 6 7 8 9 10 ... #> $ n : num 1000 500 250 200 150 100 75 50 20 1000 ... #> $ mux : num 100 100 100 100 100 100 100 100 100 100 ... #> $ mum : num 100 100 100 100 100 100 100 100 100 100 ... #> $ muy : num 100 100 100 100 100 100 100 100 100 100 ... #> $ 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 ... #> $ sigma2x: num 225 225 225 225 225 225 225 225 225 225 ... #> $ sigma2m: num 225 225 225 225 225 225 225 225 225 225 ... #> $ sigma2y: num 225 225 225 225 225 225 225 225 225 225 ... #> $ reps : num 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 ...