manymome.table

Shu Fai Cheung & Sing-Hang Cheung

2024-03-04

1 Introduction

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.

2 Several Indirect Effects

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.

library(manymome.table)
ft_all <- as_flextable(out_all)
ft_all

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.

3 Conditional Indirect Effects

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.

library(manymome.table)
ft_cond <- as_flextable(out_cond)
ft_cond

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.

4 Other Features

4.1 Further Processing by 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:

save_as_docx(ft_cond, "conditional_effects.docx")

Please refer to the documentation of flextable for further information.

4.2 Other Options

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.