QuantPsyc (version 1.6)

proxInd.ef: Simple Mediation for use in Bootstrapping

Description

Calculates the indirect effect from proximal.med in a form useful to send to boot

Usage

proxInd.ef(data, i)

Arguments

data

data.frame used in proximal.med

i

i is a 'count' placeholder necessary for use in boot

Value

indirect effect that is passed to boot for each bootstrap sample

Details

This function is not useful of itself. It is specifically created as an intermediate step in bootstrapping the indirect effect.

References

Davison, A. C. & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge, UK: Cambridge University Press.

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.

See Also

proximal.med

Examples

# NOT RUN {
# }
# NOT RUN {
require(boot)
data(tra)
tmp.tra <- tra
names(tmp.tra) <- c('x','z','m','y')
med.boot <- boot(tmp.tra, proxInd.ef, R=999)
sort(med.boot$t)[c(25,975)]	#95% CI
plot(density(med.boot$t))	# Distribution of bootstapped indirect effect
summary(med.boot$t)

# }
# NOT RUN {
# }