I'm trying to bootstrap confidence intervals for my estimates of the indirect effects of a mediation model. Because it is actually (first-stage, a-path) moderated mediation, I need to actually do this three times, to find the indirect effect when my moderator (
fiscal below) is at the mean(fiscal) and +- 1 standard deviation.
I have estimated my model in two ways:
1. Using the
summary(model_m <- lm(unaccept_i ~ condition*fiscal, data=d1)) summary(model_y <- lm(redis_i ~ condition*fiscal + unaccept_i, data=d1)) mod_med <- mediation::mediate(model_m, model_y, covariates = list(fiscal = 0), treat="condition", mediator="unaccept_i", boot=T, sims = 5000)
2. Using the
psy_med <- psych::mediate(redis_i ~ condition*fiscal + (unaccept_i), data=d1, plot=F, n.iter=5000)
I can either try to access the indirect effects from either of the two approaches above (if it that's possible? It's unclear) or by directly calculating it myself:
#conditional effect of X on Y through M: (a1 + a3*W)*b a1 <- mod_med$model.m$coefficients a3 <- mod_med$model.m$coefficients b <- mod_med$model.y$coefficients cond_fx_pap <- tibble(fis_val = c(mean(d1$fiscal)-sd(d1$fiscal), mean(d1$fiscal), mean(d1$fiscal)+sd(d1$fiscal)), cond_ind_fx = (a1 + a3*fis_val)*b)
The only problem is that this approach doesn't give me confidence intervals.
Is anyone able to figure out how to bootstrap/Monte Carlo simulate confidence intervals so I can plot the conditional indirect effects (a*b) at the mean and +- 1 SD of my moderator?
Thanks SO much in advance!