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Perform a simple margins analysis.

Source: R/simple_margins.R
sim_margins.Rd

sim_margins conducts a simple margins analysis for the purposes of understanding two- and three-way interaction effects in linear regression.

Usage

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("jtools-digits", default = 2),
  pvals = TRUE,
  confint = FALSE,
  ci.width = 0.95,
  cluster = NULL,
  modx.labels = NULL,
  mod2.labels = NULL,
  ...
)

Arguments

model

A regression model. The function is tested with lm, glm, svyglm, merMod, rq, brmsfit, stanreg models. Models from other classes may work as well but are not officially supported. The model should include the interaction of interest.

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 rlang, so programmers can use the !! syntax to pass variables instead of the verbatim names.

modx

The name of the moderator variable involved in the interaction. This can be a bare name or string. The same rlang proviso applies as with pred.

mod2

Optional. The name of the second moderator variable involved in the interaction. This can be a bare name or string. The same rlang proviso applies as with pred.

modx.values

For which values of the moderator should lines be plotted? There are two basic options:

  • A vector of values (e.g., c(1, 2, 3))

  • A single argument asking to calculate a set of values. See details below.

Default is NULL. If NULL (or mean-plus-minus), then the customary +/- 1 standard deviation from the mean as well as the mean itself are used for continuous moderators. If "plus-minus", plots lines when the moderator is at +/- 1 standard deviation without the mean. You may also choose "terciles" to split the data into equally-sized groups and choose the point at the mean of each of those groups.

If the moderator is a factor variable and modx.values is NULL, each level of the factor is included. You may specify any subset of the factor levels (e.g., c("Level 1", "Level 3")) as long as there is more than 1. The levels will be plotted in the order you provide them, so this can be used to reorder levels as well.

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 modx.values.

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., log(x)) or polynomial terms (e.g., poly(x, 2)). You will see a warning if the function detects problems that would likely be solved by providing the data with this argument and the function will attempt to retrieve the original data from the global environment.

cond.int

Should conditional intercepts be printed in addition to the slopes? Default is FALSE.

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 vce = "bootstrap", the number of bootstrap iterations. If vce = "simulation", the number of simulated effects to draw. Ignored otherwise.

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 options("jtools-digits" = digits) where digits is the desired number.

pvals

Show p values? If FALSE, these are not printed. Default is TRUE.

confint

Show confidence intervals instead of standard errors? Default is FALSE.

ci.width

A number between 0 and 1 that signifies the width of the desired confidence interval. Default is .95, which corresponds to a 95% confidence interval. Ignored if confint = FALSE.

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 modx.values argument. If NULL, the values themselves are used as labels unless modx,values is also NULL. In that case, "+1 SD" and "-1 SD" are used.

mod2.labels

A character vector of labels for each level of the 2nd moderator values, provided in the same order as the mod2.values argument. If NULL, the values themselves are used as labels unless mod2.values is also NULL. In that case, "+1 SD" and "-1 SD" are used.

...

ignored.

Value

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

Details

This allows the user to perform a simple margins analysis for the purpose of probing interaction effects in a linear regression. Two- and three-way interactions are supported, though one should be warned that three-way 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.

References

Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400. 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. doi:10.1111/j.1540-5907.2012.00602.x

See also

margins::margins()

Other interaction tools: johnson_neyman(), probe_interaction(), sim_slopes()

Author

Jacob Long jacob.long@sc.edu