| visual_mc {diffcor} | R Documentation |
Visualization of the simulated parameters
Description
To evaluate the quality of the Monte Carlo simulation beyond bias and coverage parameters (Muthén & Muthén, 2002), it can be helpful to also inspect the simulated parameters visually. To this end, visual_mc() can be used to visualize the simulated parameters (including corresponding confidence intervals) in relation to the targeted parameter.
Usage
visual_mc(rho,
n,
alpha = .05,
n.intervals = 100,
seed = 1234)Arguments
rho |
Targeted correlation coefficient of the simulation. |
n |
An integer reflecting the sample size. |
alpha |
Type I error. Default is .05. |
n.intervals |
An integer reflecting the number of simulated parameters that should be visualized in the graphic. Default is 100. |
seed |
To make the results reproducible, a random seed is specified. |
Value
A plot in which the targeted correlation coefficient is visualized with a dashed red line and the simulated correlation coefficients are visualized by black squares and confidence intervals (level depending on the specification made in the argument alpha).
Author(s)
Christian Blötner c.bloetner@gmail.com
References
Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8
Examples
visual_mc(rho = .25,
n = 300,
alpha = .05,
n.intervals = 100,
seed = 1234)