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Proportion of Effect Mediated

Source: R/indirect_proportion.R
indirect_proportion.Rd

It computes the proportion of effect mediated along a pathway.

Usage

indirect_proportion(x, y, m = NULL, fit = NULL)

Arguments

x

The name of the x variable. Must be supplied as a quoted string.

y

The name of the y variable. Must be supplied as a quoted string.

m

A vector of the variable names of the mediator(s). The path goes from the first mediator successively to the last mediator. Cannot be NULL for this function.

fit

The fit object. Can be a lavaan::lavaan object or a list of lm() outputs. It can also be a lavaan.mi object returned by semTools::runMI() or its wrapper, such as semTools::sem.mi().

Value

An indirect_proportion class object. It is a list-like object with these major elements:

  • proportion: The proportion of effect mediated.

  • x: The name of the x-variable.

  • y: The name of the y-variable.

  • m: A character vector of the mediator(s) along a path. The path runs from the first element to the last element.

This class has a print method and a coef method.

Details

The proportion of effect mediated along a path from x to y is the indirect effect along this path divided by the total effect from x to y (Alwin & Hauser, 1975). This total effect is equal to the sum of all indirect effects from x to y and the direct effect from x to y.

To ensure that the proportion can indeed be interpreted as a proportion, this function computes the the proportion only if the signs of all the indirect and direct effects from x to y are same (i.e., all effects positive or all effects negative).

References

Alwin, D. F., & Hauser, R. M. (1975). The decomposition of effects in path analysis. American Sociological Review, 40(1), 37. doi:10.2307/2094445

See also

print.indirect_proportion() for the print method, and coef.indirect_proportion() for the coef method.

Examples


library(lavaan)
dat <- data_med
head(dat)
#>           x        m        y       c1       c2
#> 1  9.931992 17.89644 20.73893 1.426513 6.103290
#> 2  8.331493 17.92150 22.91594 2.940388 3.832698
#> 3 10.327471 17.83178 22.14201 3.012678 5.770532
#> 4 11.196969 20.01750 25.05038 3.120056 4.654931
#> 5 11.887811 22.08645 28.47312 4.440018 3.959033
#> 6  8.198297 16.95198 20.73549 2.495083 3.763712
mod <-
"
m ~ x + c1 + c2
y ~ m + x + c1 + c2
"
fit <- sem(mod, dat, fixed.x = FALSE)
out <- indirect_proportion(x = "x",
                           y = "y",
                           m = "m",
                           fit = fit)
out
#> ==== Proportion of Effect Mediated ====
#> 
#> Path: 		 x -> m -> y 
#> Proportion: 	 0.591 
#> Indirect Effect: 0.733 
#> Total Effect: 	 1.241 
#> 
#> Note:
#> Use coef() to extract the proportion.
#> 
#> All indirect path(s):
#> x -> m -> y