R: Index of Moderated Mediation and Index of Moderated Moderated...

index_of_mome {manymome}

R Documentation

Index of Moderated Mediation and Index of Moderated Moderated Mediation

Description

It computes the index of moderated mediation and the index of moderated moderated mediation proposed by Hayes (2015, 2018).

Usage

index_of_mome(
  x,
  y,
  m = NULL,
  w = NULL,
  fit = NULL,
  boot_ci = FALSE,
  level = 0.95,
  boot_out = NULL,
  R = 100,
  seed = NULL,
  progress = TRUE,
  mc_ci = FALSE,
  mc_out = NULL,
  ci_type = NULL,
  ci_out = NULL,
  boot_type = c("perc", "bc"),
  ...
)

index_of_momome(
  x,
  y,
  m = NULL,
  w = NULL,
  z = NULL,
  fit = NULL,
  boot_ci = FALSE,
  level = 0.95,
  boot_out = NULL,
  R = 100,
  seed = NULL,
  progress = TRUE,
  mc_ci = FALSE,
  mc_out = NULL,
  ci_type = NULL,
  ci_out = NULL,
  boot_type = c("perc", "bc"),
  ...
)

Arguments

`x`	Character. The name of the predictor at the start of the path.
`y`	Character. The name of the outcome variable at the end of the path.
`m`	A vector of the variable names of the mediator(s). The path goes from the first mediator successively to the last mediator. If `NULL`, the default, the path goes from `x` to `y`.
`w`	Character. The name of the moderator.
`fit`	The fit object. Can be a lavaan::lavaan object, a list of `lm()` outputs, or an object created by `lm2list()`. It can also be a `lavaan.mi` object returned by `semTools::runMI()` or its wrapper, such as `semTools::sem.mi()`.
`boot_ci`	Logical. Whether bootstrap confidence interval will be formed. Default is `FALSE`.
`level`	The level of confidence for the bootstrap confidence interval. Default is .95.
`boot_out`	If `boot_ci` is `TRUE`, users can supply pregenerated bootstrap estimates. This can be the output of `do_boot()`. For `indirect_effect()` and `cond_indirect_effects()`, this can be the output of a previous call to `cond_indirect_effects()`, `indirect_effect()`, or `cond_indirect()` with bootstrap confidence intervals requested. These stored estimates will be reused such that there is no need to do bootstrapping again. If not supplied, the function will try to generate them from `fit`.
`R`	Integer. If `boot_ci` is `TRUE`, `boot_out` is `NULL`, and bootstrap standard errors not requested if `fit` is a lavaan object, this function will do bootstrapping on `fit`. `R` is the number of bootstrap samples. Default is 100. For Monte Carlo simulation, this is the number of replications.
`seed`	If bootstrapping or Monte Carlo simulation is conducted, this is the seed for the bootstrapping or simulation. Default is `NULL` and seed is not set.
`progress`	Logical. Display bootstrapping progress or not. Default is `TRUE`.
`mc_ci`	Logical. Whether Monte Carlo confidence interval will be formed. Default is `FALSE`.
`mc_out`	If `mc_ci` is `TRUE`, users can supply pregenerated Monte Carlo estimates. This can be the output of `do_mc()`. For `indirect_effect()` and `cond_indirect_effects()`, this can be the output of a previous call to `cond_indirect_effects()`, `indirect_effect()`, or `cond_indirect()` with Monte Carlo confidence intervals requested. These stored estimates will be reused such that there is no need to do Monte Carlo simulation again. If not supplied, the function will try to generate them from `fit`.
`ci_type`	The type of confidence intervals to be formed. Can be either `"boot"` (bootstrapping) or `"mc"` (Monte Carlo). If not supplied or is `NULL`, will check other arguments (e.g, `boot_ci` and `mc_ci`). If supplied, will override `boot_ci` and `mc_ci`.
`ci_out`	If `ci_type` is supplied, this is the corresponding argument. If `ci_type` is `"boot"`, this argument will be used as `boot_out`. If `ci_type` is `"mc"`, this argument will be used as `mc_out`.
`boot_type`	If bootstrap confidence interval is to be formed, the type of bootstrap confidence interval. The supported types are `"perc"` (percentile bootstrap confidence interval, the default and recommended type) and `"bc"` (bias-corrected, or BC, bootstrap confidence interval).
`...`	Arguments to be passed to `cond_indirect_effects()`
`z`	Character. The name of the second moderator, for computing the index of moderated moderated mediation.

Details

The function index_of_mome() computes the index of moderated mediation proposed by Hayes (2015). It supports any path in a model with one (and only one) component path moderated. For example, x->m1->m2->y with x->m1 moderated by w. It measures the change in indirect effect when the moderator increases by one unit.

The function index_of_momome() computes the index of moderated moderated mediation proposed by Hayes (2018). It supports any path in a model, with two component paths moderated, each by one moderator. For example, x->m1->m2->y with x->m1 moderated by w and m2->y moderated by z. It measures the change in the index of moderated mediation of one moderator when the other moderator increases by one unit.

Value

It returns a cond_indirect_diff-class object. This class has a print method (print.cond_indirect_diff()), a coef method for extracting the index (coef.cond_indirect_diff()), and a confint method for extracting the confidence interval if available (confint.cond_indirect_diff()).

Functions

index_of_mome(): Compute the index of moderated mediation.
index_of_momome(): Compute the index of moderated moderated mediation.

References

Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50(1), 1-22. doi:10.1080/00273171.2014.962683

Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs, 85(1), 4-40. doi:10.1080/03637751.2017.1352100

Examples


library(lavaan)
dat <- modmed_x1m3w4y1
dat$xw1 <- dat$x * dat$w1
mod <-
"
m1 ~ a * x  + f * w1 + d * xw1
y  ~ b * m1 + cp * x
ind_mome := d * b
"
fit <- sem(mod, dat,
           meanstructure = TRUE, fixed.x = FALSE,
           se = "none", baseline = FALSE)
est <- parameterEstimates(fit)

# R should be at least 2000 or even 5000 in real research.
# parallel is set to TRUE by default.
# Therefore, in research, the argument parallel can be omitted.
out_mome <- index_of_mome(x = "x", y = "y", m = "m1", w = "w1",
                          fit = fit,
                          boot_ci = TRUE,
                          R = 42,
                          seed = 4314,
                          parallel = FALSE,
                          progress = FALSE)
out_mome
coef(out_mome)
# From lavaan
print(est[19, ], nd = 8)
confint(out_mome)



library(lavaan)
dat <- modmed_x1m3w4y1
dat$xw1 <- dat$x * dat$w1
dat$m1w4 <- dat$m1 * dat$w4
mod <-
"
m1 ~ a * x  + f1 * w1 + d1 * xw1
y  ~ b * m1 + f4 * w4 + d4 * m1w4 + cp * x
ind_momome := d1 * d4
"
fit <- sem(mod, dat,
           meanstructure = TRUE, fixed.x = FALSE,
           se = "none", baseline = FALSE)
est <- parameterEstimates(fit)

# See the example of index_of_mome on how to request
# bootstrap confidence interval.
out_momome <- index_of_momome(x = "x", y = "y", m = "m1",
                              w = "w1", z = "w4",
                              fit = fit)
out_momome
coef(out_momome)
print(est[32, ], nd = 8)

[Package manymome version 0.2.2 Index]