View source: R/simple_margins.R
sim_margins  R Documentation 
sim_margins
conducts a simple margins analysis for the purposes of
understanding two and threeway interaction effects in linear regression.
sim_margins(
model,
pred,
modx,
mod2 = NULL,
modx.values = NULL,
mod2.values = NULL,
data = NULL,
cond.int = FALSE,
vce = c("delta", "simulation", "bootstrap", "none"),
iterations = 1000,
digits = getOption("jtoolsdigits", default = 2),
pvals = TRUE,
confint = FALSE,
ci.width = 0.95,
cluster = NULL,
modx.labels = NULL,
mod2.labels = NULL,
...
)
model 
A regression model. The function is tested with 
pred 
The name of the predictor variable involved
in the interaction. This can be a bare name or string. Note that it
is evaluated using 
modx 
The name of the moderator variable involved
in the interaction. This can be a bare name or string. The same

mod2 
Optional. The name of the second moderator
variable involved in the interaction. This can be a bare name or string.
The same 
modx.values 
For which values of the moderator should lines be plotted? There are two basic options:
Default is If the moderator is a factor variable and 
mod2.values 
For which values of the second moderator should the plot
be
facetted by? That is, there will be a separate plot for each level of this
moderator. Defaults are the same as 
data 
Optional, default is NULL. You may provide the data used to
fit the model. This can be a better way to get mean values for centering
and can be crucial for models with variable transformations in the formula
(e.g., 
cond.int 
Should conditional intercepts be printed in addition to the
slopes? Default is 
vce 
A character string indicating the type of estimation procedure to use for estimating variances. The default (“delta”) uses the delta method. Alternatives are “bootstrap”, which uses bootstrap estimation, or “simulation”, which averages across simulations drawn from the joint sampling distribution of model coefficients. The latter two are extremely time intensive. 
iterations 
If 
digits 
An integer specifying the number of digits past the decimal to
report in the output. Default is 2. You can change the default number of
digits for all jtools functions with

pvals 
Show p values? If 
confint 
Show confidence intervals instead of standard errors? Default
is 
ci.width 
A number between 0 and 1 that signifies the width of the
desired confidence interval. Default is 
cluster 
For clustered standard errors, provide the column name of the cluster variable in the input data frame (as a string). Alternately, provide a vector of clusters. 
modx.labels 
A character vector of labels for each level of the
moderator values, provided in the same order as the 
mod2.labels 
A character vector of labels for each level of the 2nd
moderator values, provided in the same order as the 
... 
ignored. 
This allows the user to perform a simple margins analysis for the purpose of probing interaction effects in a linear regression. Two and threeway interactions are supported, though one should be warned that threeway interactions are not easy to interpret in this way.
The function is tested with lm
, glm
, svyglm
, and merMod
inputs.
Others may work as well, but are not tested. In all but the linear model
case, be aware that not all the assumptions applied to simple slopes
analysis apply.
A list object with the following components:
slopes 
A table of coefficients for the focal predictor at each value of the moderator 
ints 
A table of coefficients for the intercept at each value of the moderator 
modx.values 
The values of the moderator used in the analysis 
Jacob Long jacob.long@sc.edu
Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373400. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1207/s15327906mbr4003_5")}
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analyses for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Hanmer, M. J., & Kalkan, K. O. (2013). Behind the curve: Clarifying the best approach to calculating predicted probabilities and marginal effects from limited dependent variable models. American Journal of Political Science, 57, 263–277. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.15405907.2012.00602.x")}
margins::margins()
Other interaction tools:
johnson_neyman()
,
probe_interaction()
,
sim_slopes()
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