Compute the difference in conditional indirect effects between two sets of levels of the moderators.

## Arguments

- output
A

`cond_indirect_effects`

-class object: The output of`cond_indirect_effects()`

.- from
A row number of

`output`

.- to
A row number of

`output`

. The change in indirect effects is computed by the change in the level(s) of the moderator(s) from Row`from`

to Row`to`

.- level
The level of confidence for the confidence interval. Default is .95.

## Value

A `cond_indirect_diff`

-class
object. This class has a `print`

method
(`print.cond_indirect_diff()`

), a
`coef`

method
(`coef.cond_indirect_diff()`

), and a
`confint`

method
(`confint.cond_indirect_diff()`

).

## Details

Ths function takes the output of
`cond_indirect_effects()`

and
computes the difference in
conditional indirect effects between
any two rows, that is, between levels
of the moderator, or two sets of
levels of the moderators when the
path has more than one moderator.

The difference is meaningful when the difference between the two levels or sets of levels are meaningful. For example, if the two levels are the mean of the moderator and one standard deviation above mean of the moderator, then this difference is the change in indirect effect when the moderator increases by one standard deviation.

If the two levels are 0 and 1, then
this difference is the index of
moderated mediation as proposed by
Hayes (2015). (This index can also be
computed directly by
`index_of_mome()`

, designed
specifically for this purpose.)

The function can also compute the change in the standardized indirect effect between two levels of a moderator or two sets of levels of the moderators.

This function is intended to be a general purpose function that allows users to compute the difference between any two levels or sets of levels that are meaningful in a context.

This function itself does not set the
levels of comparison. The levels to
be compared need to be set when
calling `cond_indirect_effects()`

.
This function extracts required
information from the output of
`cond_indirect_effects()`

.

If bootstrap or Monte Carlo
estimates are available
in the input or bootstrap
or Monte Carlo confidence
intervals are requested in calling
`cond_indirect_effects()`

,
`cond_indirect_diff()`

will also form
the percentile confidence
interval for the difference in
conditional indirect effects
using the stored estimates.

## Functions

`cond_indirect_diff()`

: Compute the difference in in conditional indirect effect between two rows in the output of`cond_indirect_effects()`

.

## 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

## See also

`index_of_mome()`

for
computing the index of moderated
mediation, `index_of_momome()`

for
computing the index of moderated
moderated mediation,
`cond_indirect_effects()`

,
`mod_levels()`

, and
`merge_mod_levels()`

for preparing
the levels to be compared.

## 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
"
fit <- sem(mod, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
# Create levels of w1, the moderators
w1levels <- mod_levels("w1", fit = fit)
w1levels
#> w1
#> M+1.0SD 1.2280576
#> Mean 0.2589999
#> M-1.0SD -0.7100578
# Conditional effects from x to y when w1 is equal to each of the levels
boot_out <- fit2boot_out_do_boot(fit, R = 40, seed = 4314, progress = FALSE)
out <- cond_indirect_effects(x = "x", y = "y", m = "m1",
wlevels = w1levels, fit = fit,
boot_ci = TRUE, boot_out = boot_out)
out
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind CI.lo CI.hi Sig m1~x y~m1
#> 1 M+1.0SD 1.228 0.068 -0.776 0.534 0.750 0.091
#> 2 Mean 0.259 0.048 -0.522 0.377 0.523 0.091
#> 3 M-1.0SD -0.710 0.027 -0.267 0.221 0.297 0.091
#>
#> - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by
#> nonparametric bootstrapping with 40 samples.
#> - The 'ind' column shows the indirect effects.
#> - ‘m1~x’,‘y~m1’ is/are the path coefficient(s) along the path
#> conditional on the moderator(s).
#>
out_ind <- cond_indirect_diff(out, from = 2, to = 1)
out_ind
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind CI.lo CI.hi Sig m1~x y~m1
#> 1 M+1.0SD 1.228 0.068 -0.776 0.534 0.750 0.091
#> 2 Mean 0.259 0.048 -0.522 0.377 0.523 0.091
#>
#> == Difference in Conditional Indirect Effect ==
#>
#> Levels:
#> w1
#> To: M+1.0SD 1.228
#> From: Mean 0.259
#>
#> Levels compared: Row 1 - Row 2
#>
#> Change in Indirect Effect:
#>
#> x y Change CI.lo CI.hi
#> Change x y 0.021 -0.255 0.166
#>
#> - [CI.lo, CI.hi]: 95% percentile confidence interval.
#>
coef(out_ind)
#> y~m1~x
#> 0.0206078
confint(out_ind)
#> 2.50% 97.50%
#> y~m1~x -0.2547857 0.1662861
```