Computes the Indirect Effect for a simple 3 variable mediation model: X -> M -> Y assuming direct effect X -> Y
Usage
proximal.med(data)
Arguments
data
data.frame containing the variables labeled 'x', 'm', and 'y' respectively.
Value
Creates a table containing the following effects, their standard errors, and t-values :
a
Effect of X on M
b
Effect of M on Y controlling for X
t
Total effect of X on Y
t'
Direct effect of X on Y accounting for M
ab
Indirect effect of X on Y though M
Aroian
Standard error of ab using Aroian method
Goodman
Standard error of ab using Goodman method
Med.Ratio
Mediation Ratio: indirect effect / total effect
Warning
This function is primative in that it is based on a simplistic model AND forces the user to name the variables in the dataset x, m, and y.
Details
This function computes all paths in the simple 3 variable system involving the following regressions:
Y = t'X + bM, and
M = aX
where t' + ab = t
The indirect effect is computed as the product of a*b. Several formula are used for the computation of the standard error for the indirect effect (see MacKinnon et al for a comprehensive review).
As noted below, one can use this function to create the indirect effect and then utilize bootstrapping for a more accurate estimate of the standard error and model the distribution of the direct effect.
References
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.
# NOT RUN {data(tra)
tmp.tra <- tra
names(tmp.tra) <- c('x','z','m','y')
data.frame(proximal.med(tmp.tra)) ## data.frame() simple makes the table 'pretty'# }