View source: R/as_flextable.indirect_list.R
as_flextable.indirect_list  R Documentation 
The 'as_flextable' method for the output of 'manymome::many_indirect_effects()'.
## S3 method for class 'indirect_list'
as_flextable(
x,
pvalue = FALSE,
se = TRUE,
var_labels = NULL,
digits = 2,
pval_digits = 3,
use_arrow = TRUE,
indirect_raw = TRUE,
indirect_raw_ci = indirect_raw,
indirect_raw_se = indirect_raw,
group_by_x = TRUE,
group_by_y = TRUE,
y_first = TRUE,
total_indirect = TRUE,
footnote = TRUE,
pcut = 0.001,
...
)
x 
The object to be converted.
Should be of the class 
pvalue 
If bootstrap confidence
intervals are stored, whether
asymmetric pvalues are reported.
Default is 
se 
Whether standard errors
are reported if confidence intervals
are stored. Default is 
var_labels 
A named vectors.
Used to replace variable names by
other names when generating the
table. For example,

digits 
The number of digits to be displayed for most numerical columns, such as effect estimates, standard errors, and confidence intervals. Default is 2. 
pval_digits 
The number of digits to be displayed for the pvalue column, if present. Default is 3. 
use_arrow 
If 
indirect_raw 
If 
indirect_raw_ci 
If 
indirect_raw_se 
If 
group_by_x 
If 
group_by_y 
If 
y_first 
If group by both
x and yvariables, group by
yvariables first if 
total_indirect 
If 
footnote 
If 
pcut 
Any pvalue less than

... 
Additional arguments. Ignored. 
It converts an indirect_list
object,
which is usually created by
manymome::many_indirect_effects()
,
to a flextable
object. The output
can be further modified by functions
from the package flextable
.
A flextable
object.
library(flextable)
library(manymome)
data(data_med_complicated)
lm_m11 < lm(m11 ~ x1 + x2, data_med_complicated)
lm_m2 < lm(m2 ~ x1 + x2, data_med_complicated)
lm_y1 < lm(y1 ~ m11 + m2 + x1 + x2, data_med_complicated)
fit < lm2list(lm_m11, lm_m2, lm_y1)
# All indirect paths
paths < all_indirect_paths(fit,
x = c("x1", "x2"),
y = c("y1"))
# Indirect paths from x1 to y1
paths_x1y1 < all_indirect_paths(fit,
x = c("x1"),
y = c("y1"))
# Indirect effect estimates
ind < many_indirect_effects(paths,
fit = fit)
ft_ind < as_flextable(ind)
ft_ind
ft_ind < as_flextable(ind, group_by_x = FALSE)
ft_ind
ind_x1y1 < many_indirect_effects(paths_x1y1,
fit = fit)
ft_ind_x1y1 < as_flextable(ind_x1y1)
ft_ind_x1y1
# Should set R to 5000 or 10000 in real research
boot_out_lm < do_boot(fit,
R = 100,
seed = 54532,
parallel = FALSE,
progress = FALSE)
ind_x1y1_ci < many_indirect_effects(paths_x1y1,
fit = fit,
boot_ci = TRUE,
boot_out = boot_out_lm)
ft_ind_x1y1_ci < as_flextable(ind_x1y1_ci)
ft_ind_x1y1_ci
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