Check all indirect paths in a model and
return them as a list of arguments of `x`

, `y`

,
and `m`

, to be used by `indirect_effect()`

.

## Arguments

- fit
A fit object. Either the output of

`lavaan::lavaan()`

or its wrapper such as`lavaan::sem()`

, or a list of the output of`lm()`

or the output of`lm2list()`

.- exclude
A character vector of variables to be excluded in the search, such as control variables.

- x
A character vector of variables that will be included as the

`x`

variables. If supplied, only paths that start from these variables will be included in the search. If`NULL`

, the default, then all variables that are one of the predictors in at least one regression equation will be included in the search.- y
A character vector of variables that will be included as the

`y`

variables. If supplied, only paths that start from these variables will be included in the search. If`NULL`

, the default, then all variables that are the outcome variables in at least one regression equation will be included in the search.- all_paths
An

`all_paths`

-class object. For example, the output of`all_indirect_paths()`

.

## Value

`all_indirect_paths()`

returns
a list of the class `all_paths`

. Each argument is a
list of three character vectors,
`x`

, the name of the predictor that starts a path, `y`

,
the name of the outcome that ends a path, and `m`

, a
character vector of one or more names of the mediators,
from `x`

to `y`

. This class has a print method.

`all_paths_to_df()`

returns a data frame with three
columns, `x`

, `y`

, and `m`

, which can be used by
functions such as `indirect_effect()`

.

## Details

It makes use of `igraph::all_simple_paths()`

to identify paths in a model.

## Functions

`all_indirect_paths()`

: Enumerate all indirect paths.`all_paths_to_df()`

: Convert the output of`all_indirect_paths()`

to a data frame with three columns:`x`

,`y`

, and`m`

.

## Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

## Examples

```
library(lavaan)
#> This is lavaan 0.6-15
#> lavaan is FREE software! Please report any bugs.
data(data_serial_parallel)
mod <-
"
m11 ~ x + c1 + c2
m12 ~ m11 + x + c1 + c2
m2 ~ x + c1 + c2
y ~ m12 + m2 + m11 + x + c1 + c2
"
fit <- sem(mod, data_serial_parallel,
fixed.x = FALSE)
# All indirect paths
out1 <- all_indirect_paths(fit)
out1
#> Call:
#> all_indirect_paths(fit = fit)
#> Path(s):
#> path
#> 1 m11 -> m12 -> y
#> 2 x -> m11 -> m12
#> 3 x -> m11 -> m12 -> y
#> 4 x -> m11 -> y
#> 5 x -> m12 -> y
#> 6 x -> m2 -> y
#> 7 c1 -> m11 -> m12
#> 8 c1 -> m11 -> m12 -> y
#> 9 c1 -> m11 -> y
#> 10 c1 -> m12 -> y
#> 11 c1 -> m2 -> y
#> 12 c2 -> m11 -> m12
#> 13 c2 -> m11 -> m12 -> y
#> 14 c2 -> m11 -> y
#> 15 c2 -> m12 -> y
#> 16 c2 -> m2 -> y
names(out1)
#> [1] "m11 -> m12 -> y" "x -> m11 -> m12" "x -> m11 -> m12 -> y"
#> [4] "x -> m11 -> y" "x -> m12 -> y" "x -> m2 -> y"
#> [7] "c1 -> m11 -> m12" "c1 -> m11 -> m12 -> y" "c1 -> m11 -> y"
#> [10] "c1 -> m12 -> y" "c1 -> m2 -> y" "c2 -> m11 -> m12"
#> [13] "c2 -> m11 -> m12 -> y" "c2 -> m11 -> y" "c2 -> m12 -> y"
#> [16] "c2 -> m2 -> y"
# Exclude c1 and c2 in the search
out2 <- all_indirect_paths(fit, exclude = c("c1", "c2"))
out2
#> Call:
#> all_indirect_paths(fit = fit, exclude = c("c1", "c2"))
#> Path(s):
#> path
#> 1 m11 -> m12 -> y
#> 2 x -> m11 -> m12
#> 3 x -> m11 -> m12 -> y
#> 4 x -> m11 -> y
#> 5 x -> m12 -> y
#> 6 x -> m2 -> y
names(out2)
#> [1] "m11 -> m12 -> y" "x -> m11 -> m12" "x -> m11 -> m12 -> y"
#> [4] "x -> m11 -> y" "x -> m12 -> y" "x -> m2 -> y"
# Exclude c1 and c2, and only consider paths start
# from x and end at y
out3 <- all_indirect_paths(fit, exclude = c("c1", "c2"),
x = "x",
y = "y")
out3
#> Call:
#> all_indirect_paths(fit = fit, exclude = c("c1", "c2"), x = "x",
#> y = "y")
#> Path(s):
#> path
#> 1 x -> m11 -> m12 -> y
#> 2 x -> m11 -> y
#> 3 x -> m12 -> y
#> 4 x -> m2 -> y
names(out3)
#> [1] "x -> m11 -> m12 -> y" "x -> m11 -> y" "x -> m12 -> y"
#> [4] "x -> m2 -> y"
```