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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 5.91Th 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 -> yEstimate 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 4.01For 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.
flextableThe 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_cond2Path: 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.