Computes a Monte Carlo confidence interval (500,000 trials) for a population unstandardized indirect effect in a path model and a Sobel standard error. This function is not recommended for a standardized indirect effect. The Monte Carlo method is general in that the slope estimates and standard errors do not need to be OLS estimates with homoscedastic standard errors. For example, LAD slope estimates and their standard errors, OLS slope estimates and heteroscedastic-consistent standard errors, and (in models with no direct effects) distribution-free Theil-Sen slope estimates with recovered standard errors also could be used.
ci.indirect(alpha, b1, b2, se1, se2)alpha level for 1-alpha confidence
unstandardized slope estimate for first path
unstandardized slope estimate for second path
standard error for b1
standard error for b2
Returns a 1-row matrix. The columns are:
Estimate - estimated indirect effect
SE - standard error
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
ci.indirect (.05, 2.48, 1.92, .586, .379)
#> Estimate SE LL UL
#> 4.7616 1.466064 2.172587 7.958743
# Should return (within sampling error):
# Estimate SE LL UL
# 4.7616 1.625282 2.178812 7.972262