monteCarloMed: Monte Carlo Confidence Intervals to Test Complex Indirect...

Description Usage Arguments Details Value Author(s) References Examples

View source: R/monteCarloMed.R

Description

This function takes an expression for an indirect effect, the parameters and standard errors associated with the expression and returns a confidence interval based on a Monte Carlo test of mediation (MacKinnon, Lockwood, & Williams, 2004).

Usage

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monteCarloMed(expression, ..., ACM = NULL, object = NULL, rep = 20000,
  CI = 95, plot = FALSE, outputValues = FALSE)

Arguments

expression

A character scalar representing the computation of an indirect effect. Different parameters in the expression should have different alphanumeric values. Expressions can use either addition (+) or multiplication (*) operators.

...

Parameter estimates for all parameters named in expression. The order of parameters should follow from expression (the first parameter named in expression should be the first parameter listed in ...). Alternatively ... can be a vector of parameter estimates.

ACM

A matrix representing the asymptotic covariance matrix of the parameters described in expression. This matrix should be a symetric matrix with dimensions equal to the number of parameters names in expression. Information on finding the ACOV is popular SEM software is described below.)

object

A lavaan model object fitted after running the running the cfa, sem, growth, or lavaan functions. The model must have parameters labelled with the same labels used in expression. When using this option do not specify values for ... or ACM

rep

The number of replications to compute. Many thousand are reccomended.

CI

Width of the confidence interval computed.

plot

Should the function output a plot of simulated values of the indirect effect?

outputValues

Should the function output all simulated values of the indirect effect?

Details

This function implements the Monte Carlo test of mediation first described in MacKinnon, Lockwood, & Williams (2004) and extends it to complex cases where the indirect effect is more than a function of two parameters. The function takes an expression for the indirect effect, randomly simulated values of the indirect effect based on the values of the parameters (and the associated standard errors) comprising the indirect effect, and outputs a confidence interval of the indirect effect based on the simulated values. For further information on the Monte Carlo test of mediation see MacKinnon, Lockwood, & Williams (2004) and Preacher & Selig (2012).

The asymptotic covariance matrix can be easily found in many popular SEM software applications.

Value

A list with two elements. The first element is the point estimate for the indirect effect. The second element is a matrix with values for the upper and lower limits of the confidence interval generated from the Monte Carlo test of mediation. If outputValues = TRUE, output will be a list with a list with the point estimate and values for the upper and lower limits of the confidence interval as the first element and a vector of simulated values of the indirect effect as the second element.

Author(s)

Corbin Quick (University of Michigan; corbinq@umich.edu)

Alexander M. Schoemann (East Carolina University; schoemanna@ecu.edu)

James P. Selig (University of New Mexico; selig@unm.edu)

Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)

References

MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1) 99–128. doi: 10.1207/s15327906mbr3901_4

Preacher, K. J., & Selig, J. P. (2010, July). Monte Carlo method for assessing multilevel mediation: An interactive tool for creating confidence intervals for indirect effects in 1-1-1 multilevel models [Computer software]. Available from http://quantpsy.org/.

Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77–98. doi: 10.1080/19312458.2012.679848

Selig, J. P., & Preacher, K. J. (2008, June). Monte Carlo method for assessing mediation: An interactive tool for creating confidence intervals for indirect effects [Computer software]. Available from http://quantpsy.org/.

Examples

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## Simple two path mediation
## Write expression of indirect effect
med <- 'a*b'
## Paramter values from analyses
aparam <- 1
bparam <- 2
## Asymptotic covariance matrix from analyses
AC <- matrix(c(.01,.00002,
               .00002,.02), nrow=2, byrow=TRUE)
## Compute CI, include a plot
monteCarloMed(med, coef1 = aparam, coef2 = bparam, outputValues = FALSE,
              plot = TRUE, ACM = AC)

## Use a vector of parameter estimates as input
aparam <- c(1,2)
monteCarloMed(med, coef1 = aparam, outputValues = FALSE,
              plot = TRUE, ACM = AC)


## Complex mediation with two paths for the indirect effect
## Write expression of indirect effect
med <- 'a1*b1 + a1*b2'
## Paramter values and standard errors from analyses
aparam <- 1
b1param <- 2
b2param <- 1
## Asymptotic covariance matrix from analyses
AC <- matrix(c(1, .00002, .00003,
               .00002, 1, .00002,
               .00003, .00002, 1), nrow = 3, byrow = TRUE)
## Compute CI do not include a plot
monteCarloMed(med, coef1 = aparam, coef2 = b1param,
              coef3 = b2param, ACM = AC)


## WORKING WITH lavaan MODELS. From the mediation tutorial:
## http://lavaan.ugent.be/tutorial/mediation.html

set.seed(1234)
X <- rnorm(100)
M <- 0.5*X + rnorm(100)
Y <- 0.7*M + rnorm(100)
Data <- data.frame(X = X, Y = Y, M = M)
model <- ' # direct effect
Y ~ c*X
# mediator
M ~ a*X
Y ~ b*M
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
fit <- sem(model, data = Data)

med <- 'a*b'
## Automatically extract information from lavaan object
monteCarloMed(med, object = fit)

## or (unnecessary) manually extract the information first
myParams <- c("a","b")
myCoefs <- coef(fit)[myParams]
myACM <- vcov(fit)[myParams, myParams]
monteCarloMed(med, myCoefs, ACM = myACM)

semTools documentation built on Jan. 13, 2021, 8:09 p.m.