This article is a brief illustration of how to use
`cond_indirect_effects()`

from the package manymome (Cheung & Cheung,
2023) to estimate the conditional indirect effects when the model
parameters are estimate by ordinary least squares (OLS) multiple
regression using `lm()`

.

This is the sample data set used for illustration:

```
library(manymome)
<- data_med_mod_a
dat print(head(dat), digits = 3)
#> x w m y c1 c2
#> 1 8.58 1.57 28.9 36.9 6.03 4.82
#> 2 10.36 1.10 24.8 24.5 5.19 5.34
#> 3 10.38 2.88 37.3 38.1 4.63 5.02
#> 4 9.53 3.16 32.6 37.9 2.94 6.01
#> 5 11.34 3.84 49.2 59.0 6.12 5.05
#> 6 9.66 2.22 26.4 35.4 4.02 5.03
```

This dataset has 6 variables: one predictor (`x`

), one
mediators (`m`

), one outcome variable (`y`

), one
moderator (`w`

) and two control variables (`c1`

and `c2`

).

Suppose this is the model being fitted:

The path parameters can be estimated by two multiple regression models:

```
<- lm(m ~ x*w + c1 + c2, dat)
lm_m <- lm(y ~ m + x + c1 + c2, dat) lm_y
```

These are the estimates of the regression coefficient of the paths:

```
# ###### Predict m ######
#
summary(lm_m)
#>
#> Call:
#> lm(formula = m ~ x * w + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -8.5621 -2.0065 -0.2142 1.7618 10.4270
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 16.4910 12.1039 1.362 0.1763
#> x 0.0959 1.1958 0.080 0.9362
#> w -3.4871 4.7907 -0.728 0.4685
#> c1 0.5372 0.4162 1.291 0.2000
#> c2 -0.1533 0.4211 -0.364 0.7165
#> x:w 0.9785 0.4794 2.041 0.0441 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.998 on 94 degrees of freedom
#> Multiple R-squared: 0.7479, Adjusted R-squared: 0.7345
#> F-statistic: 55.79 on 5 and 94 DF, p-value: < 2.2e-16
#
# ###### Predict y ######
#
summary(lm_y)
#>
#> Call:
#> lm(formula = y ~ m + x + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -9.4396 -2.8156 -0.3145 2.3231 11.2849
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 4.50635 5.40625 0.834 0.407
#> m 0.95867 0.05806 16.512 <2e-16 ***
#> x -0.01980 0.50578 -0.039 0.969
#> c1 0.68241 0.44110 1.547 0.125
#> c2 -0.49573 0.44565 -1.112 0.269
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 4.229 on 95 degrees of freedom
#> Multiple R-squared: 0.7669, Adjusted R-squared: 0.7571
#> F-statistic: 78.15 on 4 and 95 DF, p-value: < 2.2e-16
```

Although not mandatory, it is recommended to combine the models into
one object (a system of regression models) using
`lm2list()`

:

```
<- lm2list(lm_m, lm_y)
fit_lm
fit_lm#>
#> The models:
#> m ~ x * w + c1 + c2
#> y ~ m + x + c1 + c2
```

Simply use the `lm()`

outputs as arguments. Order does not
matter. To ensure that the regression outputs can be validly combined,
`lm2list()`

will also check:

whether the same sample is used in all regression analysis (not just same sample size, but the same set of cases), and

whether the models are “connected”, to ensure that the regression outputs can be validly combined.

To form nonparametric bootstrap confidence interval for effects to be
computed, `do_boot()`

can be used to generate bootstrap
estimates for all regression coefficients first. These estimates can be
reused for any effects to be estimated.

```
<- do_boot(fit_lm,
boot_out_lm R = 100,
seed = 54532,
ncores = 1)
```

Please see `vignette("do_boot")`

or the help page of
`do_boot()`

on how to use this function. In real research,
`R`

, the number of bootstrap samples, should be set to 2000
or even 5000. The argument `ncores`

can usually be omitted
unless users want to manually control the number of CPU cores used in
parallel processing.

We can now use `cond_indirect_effects()`

to estimate the
indirect effects for different levels of the moderator (`w`

)
and form their bootstrap confidence interval. By reusing the generated
bootstrap estimates, there is no need to repeat the resampling.

Suppose we want to estimate the indirect effect from `x`

to `y`

through `m`

, conditional on
`w`

:

(Refer to `vignette("manymome")`

and the help page of
`cond_indirect_effects()`

on the arguments.)

```
<- cond_indirect_effects(wlevels = "w",
out_xmy_on_w x = "x",
y = "y",
m = "m",
fit = fit_lm,
boot_ci = TRUE,
boot_out = boot_out_lm)
out_xmy_on_w#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m -> y
#> Conditional on moderator(s): w
#> Moderator(s) represented by: w
#>
#> [w] (w) ind CI.lo CI.hi Sig m~x y~m
#> 1 M+1.0SD 3.164 3.060 2.168 4.039 Sig 3.192 0.959
#> 2 Mean 2.179 2.136 1.407 2.925 Sig 2.228 0.959
#> 3 M-1.0SD 1.194 1.212 -0.288 2.564 1.265 0.959
#>
#> - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by
#> nonparametric bootstrapping with 100 samples.
#> - The 'ind' column shows the indirect effects.
#> - 'm~x','y~m' is/are the path coefficient(s) along the path conditional
#> on the moderators.
```

When `w`

is one standard deviation below mean, the
indirect effect is 1.212, with 95% confidence interval [-0.288,
2.564].

When `w`

is one standard deviation above mean, the
indirect effect is 3.060, with 95% confidence interval [2.168,
4.039].

Note that any conditional indirect path in the model can be estimated
this way. There is no limit on the path to be estimated, as long as all
required path coefficients are in the model.
`cond_indirect_effects()`

will also check whether a path is
valid. However, for complicated models, structural equation modelling
may be a more flexible approach than multiple regression.

Not covered here, but the index of moderated moderated mediation can
also be estimated in models with two moderators on the same path,
estimated by regression. See `vignette("manymome")`

for an
example.

The function `index_of_mome()`

can be used to compute the
index of moderated mediation of `w`

on the path
`x -> m -> y`

:

(Refer to `vignette("manymome")`

and the help page of
`index_of_mome()`

on the arguments.)

```
<- index_of_mome(x = "x",
out_mome y = "y",
m = "m",
w = "w",
fit = fit_lm,
boot_ci = TRUE,
boot_out = boot_out_lm)
out_mome#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m -> y
#> Conditional on moderator(s): w
#> Moderator(s) represented by: w
#>
#> [w] (w) ind CI.lo CI.hi Sig m~x y~m
#> 1 1 1 1.030 -0.622 2.543 1.074 0.959
#> 2 0 0 0.092 -2.389 2.434 0.096 0.959
#>
#> == Index of Moderated Mediation ==
#>
#> Levels compared: Row 1 - Row 2
#>
#> x y Index CI.lo CI.hi
#> Index x y 0.938 0.178 1.732
#>
#> - [CI.lo, CI.hi]: 95% percentile confidence interval.
```

In this model, the index of moderated mediation is 0.938, with 95%
bootstrap confidence interval [0.178, 1.732]. The indirect effect of
`x`

on `y`

through `m`

significantly
changes when `w`

increases by one unit.

The standardized conditional indirect effect from `x`

to
`y`

through `m`

conditional on `w`

can
be estimated by setting `standardized_x`

and
`standardized_y`

to `TRUE`

:

```
<- cond_indirect_effects(wlevels = "w",
std_xmy_on_w x = "x",
y = "y",
m = "m",
fit = fit_lm,
boot_ci = TRUE,
boot_out = boot_out_lm,
standardized_x = TRUE,
standardized_y = TRUE)
std_xmy_on_w#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m -> y
#> Conditional on moderator(s): w
#> Moderator(s) represented by: w
#>
#> [w] (w) std CI.lo CI.hi Sig m~x y~m ind
#> 1 M+1.0SD 3.164 0.318 0.220 0.437 Sig 3.192 0.959 3.060
#> 2 Mean 2.179 0.222 0.134 0.309 Sig 2.228 0.959 2.136
#> 3 M-1.0SD 1.194 0.126 -0.031 0.260 1.265 0.959 1.212
#>
#> - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by
#> nonparametric bootstrapping with 100 samples.
#> - std: The standardized indirect effects.
#> - ind: The unstandardized indirect effects.
#> - 'm~x','y~m' is/are the path coefficient(s) along the path conditional
#> on the moderators.
```

The standardized indirect effect is 0.126, with 95% confidence interval [-0.031, 0.260].

After the regression coefficients are estimated,
`cond_indirect_effects()`

, `indirect_effect()`

,
and related functions are used in the same way as for models fitted by
`lavaan::sem()`

. The levels for the moderators are controlled
by `mod_levels()`

and related functions in the same way
whether a model is fitted by `lavaan::sem()`

or
`lm()`

. Pplease refer to other articles (e.g.,
`vignette("manymome")`

and
`vignette("mod_levels")`

) on how to estimate effects in other
model analyzed by multiple regression.

Cheung, S. F., & Cheung, S.-H. (2023). *manymome*: An R
package for computing the indirect effects, conditional effects, and
conditional indirect effects, standardized or unstandardized, and their
bootstrap confidence intervals, in many (though not all) models.
*Behavior Research Methods*. https://doi.org/10.3758/s13428-023-02224-z