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,
  skip_indicators = TRUE
)

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 `lavaan.mi::lavaan.mi()` or its wrapper, such as `lavaan.mi::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 moderates one of the component path. Setting 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.
`skip_indicators`	Whether observed indicators are skipped from the search for product terms. Default is `TRUE`.

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 a 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 Sept. 3, 2026, 9:08 a.m.