Computes the 'total indirect effect' from
distal.med for use in
data.frame used in
This function is not useful of itself. It is specifically created as an intermediate step in bootstrapping the indirect effect.
indirect effect that is passed to boot for each bootstrap sample
Thomas D. Fletcher firstname.lastname@example.org
Davison, A. C. & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge, UK: Cambridge University Press.
Fletcher, T. D. (2006, August). Methods and approaches to assessing distal mediation. Paper presented at the 66th annual meeting of the Academy of Management, Atlanta, GA.
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.
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limit for indirect effect: distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99-128.
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cormat <- matrix (c(1,.3,.15,.075,.3,1,.3,.15,.15,.3,1,.3,.075,.15,.3,1),ncol=4) require(MASS) d200 <- data.frame(mvrnorm(200, mu=c(0,0,0,0), cormat)) names(d200) <- c("x","m1","m2","y") require(boot) distmed.boot <- boot(d200, distInd.ef, R=999) sort(distmed.boot$t)[c(25,975)] #95% CI plot(density(distmed.boot$t)) # Distribution of bootstapped indirect effect summary(distmed.boot$t)