indirect_i: Indirect Effect (No Bootstrapping)
In manymome: Mediation, Moderation and Moderated-Mediation After Model Fitting

indirect_i

R Documentation

Indirect Effect (No Bootstrapping)

Description

It computes an indirect effect, optionally conditional on the value(s) of moderator(s) if present.

Usage

indirect_i(
  x,
  y,
  m = NULL,
  fit = NULL,
  est = NULL,
  implied_stats = NULL,
  wvalues = NULL,
  standardized_x = FALSE,
  standardized_y = FALSE,
  computation_digits = 5,
  prods = NULL,
  get_prods_only = FALSE,
  data = NULL,
  expand = TRUE,
  warn = TRUE,
  allow_mixing_lav_and_obs = TRUE,
  group = NULL,
  est_vcov = NULL,
  df_residual = NULL
)

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`.
`fit`	The fit object. Currently only supports lavaan::lavaan objects. Support for lists of `lm()` output is implemented by high level functions such as `indirect_effect()` and `cond_indirect_effects()`. It can also be a `lavaan.mi` object returned by `semTools::runMI()` or its wrapper, such as `semTools::sem.mi()`.
`est`	The output of `lavaan::parameterEstimates()`. If `NULL`, the default, it will be generated from `fit`. If supplied, `fit` will be ignored.
`implied_stats`	Implied means, variances, and covariances of observed variables and latent variables (if any), of the form of the output of `lavaan::lavInspect()` with `what` set to `"implied"`, but with means extracted with `what` set to `"mean.ov"` and `"mean.lv"`. The standard deviations are extracted from this object for standardization. Default is `NULL`, and implied statistics will be computed from `fit` if required.
`wvalues`	A numeric vector of named elements. The names are the variable names of the moderators, and the values are the values to which the moderators will be set to. Default is `NULL`.
`standardized_x`	Logical. Whether `x` will be standardized. Default is `FALSE`.
`standardized_y`	Logical. Whether `y` will be standardized. Default is `FALSE`.
`computation_digits`	The number of digits in storing the computation in text. Default is 3.
`prods`	The product terms found. For internal use.
`get_prods_only`	IF `TRUE`, will quit early and return the product terms found. The results can be passed to the `prod` argument when calling this function. Default is `FALSE`. For internal use.
`data`	Data frame (optional). If supplied, it will be used to identify the product terms. For internal use.
`expand`	Whether products of more than two terms will be searched. `TRUE` by default. For internal use.
`warn`	If `TRUE`, the default, the function will warn against possible misspecification, such as not setting the value of a moderator which moderate one of the component path. Set this to `FALSE` will suppress these warnings. Suppress them only when the moderators are omitted intentionally.
`allow_mixing_lav_and_obs`	If `TRUE`, it accepts a path with both latent variables and observed variables. Default is `TRUE`.
`group`	Either the group number as appeared in the `summary()` or `lavaan::parameterEstimates()` output of an `lavaan`-class object, or the group label as used in the `lavaan`-class object. Used only when the number of groups is greater than one. Default is NULL.
`est_vcov`	A list of variance-covariance matrix of estimates, one for each response variable (`y`-variable). Used only for models fitted by `stats::lm()` for now. It is used to compute the standard error for a path with no mediator, and both `x` and `y` are not standardized.
`df_residual`	A numeric vector of the residual degrees of freedom for the model of each response variable (`y`-variable). Used only for models fitted by `stats::lm()` for now. It is used to compute the p-value and confidence interval for a path with no mediator and both `x` and `y` are not standardized.

Details

This function is a low-level function called by indirect_effect(), cond_indirect_effects(), and cond_indirect(), which call this function multiple times if bootstrap confidence interval is requested.

This function usually should not be used directly. It is exported for advanced users and developers

Value

It returns an indirect-class object. This class has the following methods: coef.indirect(), print.indirect(). The confint.indirect() method is used only when called by cond_indirect() or cond_indirect_effects().

Examples


library(lavaan)
dat <- modmed_x1m3w4y1
mod <-
"
m1 ~ a1 * x   + b1 * w1 + d1 * x:w1
m2 ~ a2 * m1  + b2 * w2 + d2 * m1:w2
m3 ~ a3 * m2  + b3 * w3 + d3 * m2:w3
y  ~ a4 * m3  + b4 * w4 + d4 * m3:w4
"
fit <- sem(mod, dat, meanstructure = TRUE,
           fixed.x = FALSE, se = "none", baseline = FALSE)
est <- parameterEstimates(fit)

wvalues <- c(w1 = 5, w2 = 4, w3 = 2, w4 = 3)

# Compute the conditional indirect effect by indirect_i()
indirect_1 <- indirect_i(x = "x", y = "y", m = c("m1", "m2", "m3"), fit = fit,
                       wvalues = wvalues)

# Manually compute the conditional indirect effect
indirect_2 <- (est[est$label == "a1", "est"] +
                wvalues["w1"] * est[est$label == "d1", "est"]) *
              (est[est$label == "a2", "est"] +
                wvalues["w2"] * est[est$label == "d2", "est"]) *
              (est[est$label == "a3", "est"] +
                wvalues["w3"] * est[est$label == "d3", "est"]) *
              (est[est$label == "a4", "est"] +
                wvalues["w4"] * est[est$label == "d4", "est"])
# They should be the same
coef(indirect_1)
indirect_2

manymome documentation built on Oct. 4, 2024, 5:10 p.m.