Description Usage Arguments Value Author(s) See Also Examples
Produce dot plots of selected coefficients from regression models computed in (robust) mediation analysis, or density plots of the indirect effect.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  plot_mediation(object, ...)
## S3 method for class 'boot_test_mediation'
plot_mediation(object, method = c("dot",
"density"), parm = NULL, ...)
## S3 method for class 'sobel_test_mediation'
plot_mediation(object, data,
method = c("dot", "density"), parm = c("c", "ab"), level = 0.95,
...)
## S3 method for class 'list'
plot_mediation(object, data, method = c("dot", "density"),
parm = NULL, level = 0.95, ...)
## Default S3 method:
plot_mediation(object, mapping = attr(object,
"mapping"), facets = attr(object, "facets"), ...)
## S3 method for class 'test_mediation'
autoplot(object, ...)
## S3 method for class 'test_mediation'
plot(x, ...)

object, x 
an object inheriting from class

... 
additional arguments to be passed to and from methods. 
method 
a character string specifying which plot to produce.
Possible values are 
parm 
a character string specifying the coefficients to be included in a dot plot. The default is to include the direct and the indirect effect(s). 
data 
an optional numeric vector containing the xvalues at which to evaluate the assumed normal density from Sobel's test (only used in case of a density plot). The default is to take 100 equally spaced points between the estimated indirect effect +/ three times the standard error according to Sobel's formula. 
level 
numeric; the confidence level of the confidence intervals from Sobel's test to be included in a dot plot. The default is to include 95% confidence intervals. 
mapping 
an aesthetic mapping to override the default behavior (see

facets 
a faceting formula to override the default behavior (only
used in case of a dot plot). If supplied, 
An object of class "ggplot"
(see
ggplot
).
Andreas Alfons
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  data("BSG2014")
# run fast and robust bootstrap test
robust_boot < test_mediation(BSG2014,
x = "ValueDiversity",
y = "TeamCommitment",
m = "TaskConflict",
robust = TRUE)
# create plots for robust bootstrap test
plot(robust_boot, method = "dot")
plot(robust_boot, method = "density")
# run standard bootstrap test
standard_boot < test_mediation(BSG2014,
x = "ValueDiversity",
y = "TeamCommitment",
m = "TaskConflict",
robust = FALSE)
# compare robust and standard tests
tests < list(Robust = robust_boot, Standard = standard_boot)
plot_mediation(tests, method = "dot")
plot_mediation(tests, method = "density")

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