distInd.ef {QuantPsyc} | R Documentation |
Computes the 'total indirect effect' from distal.med
for use in boot
distInd.ef(data, i)
data |
data.frame used in distal.med |
i |
i is a 'count' placeholder necessary for use in boot |
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 tom.fletcher.mp7e@statefarm.com
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.
cormat <- matrix (c(1,.3,.15,.075,.3,1,.3,.15,.15,.3,1,.3,.075,.15,.3,1),ncol=4) library(MASS) d200 <- data.frame(mvrnorm(200, mu=c(0,0,0,0), cormat)) names(d200) <- c("x","m1","m2","y") library(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)