This article is a brief illustration of how convert some results from
the package manymome (Cheung & Cheung,
2023) to publication-ready tables using the functions from manymome.table.
It assumes readers have used `manymome`

. This guide will
focus on converting the results using the `as_flextable()`

method.

The example from this article will be used, with some modifications.

This is the sample data set from `manymome`

:

```
library(manymome)
dat <- data_serial
print(head(dat), digits = 3)
#> x m1 m2 y c1 c2
#> 1 12.12 20.6 9.33 9.00 0.109262 6.01
#> 2 9.81 18.2 9.47 11.56 -0.124014 6.42
#> 3 10.11 20.3 10.05 9.35 4.278608 5.34
#> 4 10.07 19.7 10.17 11.41 1.245356 5.59
#> 5 11.91 20.5 10.05 14.26 -0.000932 5.34
#> 6 9.13 16.5 8.93 10.01 1.802727 5.91
```

Th following model is fitted in `lavaan`

:

```
library(lavaan)
#> This is lavaan 0.6-17
#> lavaan is FREE software! Please report any bugs.
mod_med <- "
m1 ~ x
m2 ~ m1 + x
y ~ m2 + m1 + x
"
fit_med <- sem(model = mod_med,
data = dat,
fixed.x = TRUE)
```

Use `all_indirect_paths()`

to identify all indirect
paths:

```
all_paths <- all_indirect_paths(fit = fit_med,
x = "x",
y = "y")
all_paths
#> Call:
#> all_indirect_paths(fit = fit_med, x = "x", y = "y")
#> Path(s):
#> path
#> 1 x -> m1 -> m2 -> y
#> 2 x -> m1 -> y
#> 3 x -> m2 -> y
```

Estimate the indirect effects, with bootstrap confidence intervals.

```
# R set to 100 just for illustration.
# Use 5000 or 10000 and set parallel to TRUE in real research.
out_all <- many_indirect_effects(paths = all_paths,
fit = fit_med,
standardized_x = TRUE,
standardized_y = TRUE,
boot_ci = TRUE,
R = 100,
seed = 12345,
parallel = FALSE,
progress = FALSE)
out_all
#>
#> == Indirect Effect(s) (Both x-variable(s) and y-variable(s) Standardized) ==
#> std CI.lo CI.hi Sig
#> x -> m1 -> m2 -> y 0.119 0.016 0.223 Sig
#> x -> m1 -> y -0.163 -0.307 -0.039 Sig
#> x -> m2 -> y -0.058 -0.208 0.024
#>
#> - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by
#> nonparametric bootstrapping with 100 samples.
#> - std: The standardized indirect effects.
#>
```

The method `as_flextable()`

can then be used to convert
the output to a flextable. To use this method, we need to load the
package `manymome.table`

first.

Path | Effect | Std. Effect | 95% CI | SE | |
---|---|---|---|---|---|

x → m1 → m2 → y | 0.25 | 0.12 | [0.02 | , 0.22] | 0.05 |

x → m1 → y | -0.34 | -0.16 | [-0.31 | , -0.04] | 0.07 |

x → m2 → y | -0.12 | -0.06 | [-0.21 | , 0.02] | 0.05 |

x → .. → y | -0.21 | -0.10 | [-0.29 | , 0.04] | 0.07 |

Note: CI = confidence interval; paths with '..' are total indirect effects; Std. Effect is completely standardized effect. |

By default, if standardized effects are requested, the unstandardized effects will also be printed when converting to a flextable.

Not demonstrated here due to speed concern, it also supports output with confidence intervals.

See `help(as_flextable.indirect_list)`

for more
information on the options available in the conversion.

The example from this article will be used, with some modifications.

This is the sample data set from `manymome:

```
dat <- data_med_mod_ab
print(head(dat), digits = 3)
#> x w1 w2 m y c1 c2
#> 1 9.27 4.97 2.66 3.46 8.80 9.26 3.14
#> 2 10.79 4.13 3.33 4.05 7.37 10.71 5.80
#> 3 11.10 5.91 3.32 4.04 8.24 10.60 5.45
#> 4 9.53 4.78 2.32 3.54 8.37 9.22 3.83
#> 5 10.00 4.38 2.95 4.65 8.39 9.58 4.26
#> 6 12.25 5.81 4.04 4.73 9.65 9.51 4.01
```

For illustration, OLS regression is used instead of structural equation modeling to fit the model:

```
m ~ x + w1 + w1x + c1 + c2
#> m ~ x + w1 + w1x + c1 + c2
y ~ m + w2 + w2m + x + c1 + c2
#> y ~ m + w2 + w2m + x + c1 + c2
lm_m <- lm(m ~ x*w1, dat)
lm_y <- lm(y ~ m*w2 + x, dat)
lm_out <- lm2list(lm_m, lm_y)
```

Compute conditional indirect effects:

```
# R set to 100 just for illustration.
# Use 5000 or 10000 and set parallel to TRUE in real research.
out_cond <- cond_indirect_effects(wlevels =c("w1", "w2"),
x = "x",
y = "y",
m = "m",
fit = lm_out,
standardized_x = TRUE,
standardized_y = TRUE,
boot_ci = TRUE,
R = 100,
seed = 12345,
parallel = FALSE,
progress = FALSE)
out_cond
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m -> y
#> Conditional on moderator(s): w1, w2
#> Moderator(s) represented by: w1, w2
#>
#> [w1] [w2] (w1) (w2) std CI.lo CI.hi Sig m~x y~m ind
#> 1 M+1.0SD M+1.0SD 6.173 4.040 0.412 0.110 0.685 Sig 0.599 0.685 0.410
#> 2 M+1.0SD M-1.0SD 6.173 2.055 0.182 -0.021 0.437 0.599 0.302 0.181
#> 3 M-1.0SD M+1.0SD 4.038 4.040 0.119 -0.089 0.393 0.173 0.685 0.118
#> 4 M-1.0SD M-1.0SD 4.038 2.055 0.052 -0.042 0.203 0.173 0.302 0.052
#>
#> - [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 method `as_flextable()`

can then be used to convert
the output to a flextable.

Path: x → m → y | ||||||||
---|---|---|---|---|---|---|---|---|

[w1] | [w2] | (w1) | (w2) | Effect | Std. Effect | 95% CI | SE | |

M+1.0SD | M+1.0SD | 6.17 | 4.04 | 0.41 | 0.41 | [0.11 | , 0.68] | 0.15 |

M+1.0SD | M-1.0SD | 6.17 | 2.06 | 0.18 | 0.18 | [-0.02 | , 0.44] | 0.11 |

M-1.0SD | M+1.0SD | 4.04 | 4.04 | 0.12 | 0.12 | [-0.09 | , 0.39] | 0.12 |

M-1.0SD | M-1.0SD | 4.04 | 2.06 | 0.05 | 0.05 | [-0.04 | , 0.20] | 0.07 |

Note: [w] is the meaning of a level of moderator 'w'; (w) is the value of a level of moderator 'w': CI = confidence interval; Std. Effect is completely standardized effect. |

By default, if standardized effects are requested, the unstandardized effects will also be printed when converting to a flextable.

Not demonstrated here due to speed concern, it also supports output with confidence intervals.

See `help(as_flextable.cond_indirect_effects)`

for more
information on the options available in the conversion.

`flextable`

The output of both methods is a flextable object. Therefore, it can
be further modified by functions for flextable. Load the package
`flextable`

first to use its functions.

For example:

```
library(flextable)
ft_cond2 <- ft_cond |>
bold(part = "header") |>
bg(i = c(1, 2), bg = "lightblue", part = "body") |>
bg(i = c(3, 4), bg = "lightgreen", part = "body")
ft_cond2
```

Path: x → m → y | ||||||||
---|---|---|---|---|---|---|---|---|

[w1] | [w2] | (w1) | (w2) | Effect | Std. Effect | 95% CI | SE | |

M+1.0SD | M+1.0SD | 6.17 | 4.04 | 0.41 | 0.41 | [0.11 | , 0.68] | 0.15 |

M+1.0SD | M-1.0SD | 6.17 | 2.06 | 0.18 | 0.18 | [-0.02 | , 0.44] | 0.11 |

M-1.0SD | M+1.0SD | 4.04 | 4.04 | 0.12 | 0.12 | [-0.09 | , 0.39] | 0.12 |

M-1.0SD | M-1.0SD | 4.04 | 2.06 | 0.05 | 0.05 | [-0.04 | , 0.20] | 0.07 |

Note: [w] is the meaning of a level of moderator 'w'; (w) is the value of a level of moderator 'w': CI = confidence interval; Std. Effect is completely standardized effect. |

The export functions from `flextable`

can also be used to
export one or more tables to an external file, such as a Word file:

Please refer to the documentation of `flextable`

for
further information.

Both `as_flextable.indirect_list()`

and
`as_flextable.cond_indirect_effects()`

have options for
customize the generation of the table. For example, if the list of
indirect paths have different predictors (x-variables) and/or different
outcome variables (y-variables), the estimates caN be grouped by x-
and/or y-variables. Please refer to the help pages for further
details.