sim_slopes: Perform a simple slopes analysis.

In jacob-long/interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions

Perform a simple slopes analysis.

Description

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

Usage

sim_slopes(
  model,
  pred,
  modx,
  mod2 = NULL,
  modx.values = NULL,
  mod2.values = NULL,
  centered = "all",
  at = NULL,
  data = NULL,
  cond.int = FALSE,
  johnson_neyman = TRUE,
  jnplot = FALSE,
  jnalpha = 0.05,
  robust = FALSE,
  digits = getOption("jtools-digits", default = 2),
  pvals = TRUE,
  confint = FALSE,
  ci.width = 0.95,
  cluster = NULL,
  modx.labels = NULL,
  mod2.labels = NULL,
  v.cov = NULL,
  v.cov.args = 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.
centered	A vector of quoted variable names that are to be mean-centered. If "all", all non-focal predictors as well as the pred variable are centered. You may instead pass a character vector of variables to center. User can also use "none" to base all predictions on variables set at 0. The response variable, modx, and mod2 variables are never centered.
at	If you want to manually set the values of other variables in the model, do so by providing a named list where the names are the variables and the list values are vectors of the values. Note that you cannot alter the values of the pred, modx, or mod2 variables and this will take precedence over the centered argument (but any variables unmentioned by at will be centered as specified by centered). For linear models, this will only change the output of the conditional intercepts.
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.
johnson_neyman	Should the Johnson-Neyman interval be calculated? Default is TRUE. This can be performed separately with johnson_neyman.
jnplot	Should the Johnson-Neyman interval be plotted as well? Default is FALSE.
jnalpha	What should the alpha level be for the Johnson-Neyman interval? Default is .05, which corresponds to a 95% confidence interval.
robust	Should robust standard errors be used to find confidence intervals for supported models? Default is FALSE, but you should specify the type of sandwich standard errors if you'd like to use them (i.e., "HC0", "HC1", and so on). If TRUE, defaults to "HC3" standard errors.
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.
v.cov	A function to calculate variances for the model. Examples could be sandwich::vcovPC().
v.cov.args	A list of arguments for the v.cov function. For whichever argument should be the fitted model, put "model".
...	Arguments passed to johnson_neyman and summ.

Details

This allows the user to perform a simple slopes 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.

For more about Johnson-Neyman intervals, see johnson_neyman.

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.

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
mods	A list containing each regression model created to estimate the conditional coefficients.
jn	If johnson_neyman = TRUE, a list of johnson_neyman objects from johnson_neyman. These contain the values of the interval and the plots. If a 2-way interaction, the list will be of length Otherwise, there will be 1 johnson_neyman object for each value of the 2nd moderator for 3-way interactions.

Author(s)

Jacob Long [e-mail]

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. \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.

See Also

interact_plot accepts similar syntax and will plot the results with ggplot.

testSlopes() from rockchalk performs a hypothesis test of differences and provides Johnson-Neyman intervals.

simpleSlope() from pequod performs a similar analysis.

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

Examples

# Using a fitted model as formula input
fiti <- lm(Income ~ Frost + Murder * Illiteracy,
  data = as.data.frame(state.x77))
sim_slopes(model = fiti, pred = Murder, modx = Illiteracy)

# With svyglm
if (requireNamespace("survey")) {
library(survey)
data(api)
dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw,
                    data = apistrat, fpc = ~fpc)
regmodel <- svyglm(api00 ~ ell * meals, design = dstrat)
sim_slopes(regmodel, pred = ell, modx = meals)

# 3-way with survey and factor input
regmodel <- svyglm(api00 ~ ell * meals * sch.wide, design = dstrat)
sim_slopes(regmodel, pred = ell, modx = meals, mod2 = sch.wide)
}

jacob-long/interactions documentation built on Aug. 1, 2024, 8:47 a.m.