probe_interaction
Probe interaction effects via simple slopes and plotting
probe_interaction
is a convenience function that allows users to call
both sim_slopes
and interact_plot
with a single
call.
Usage
probe_interaction(model, pred, modx, mod2 = 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 withpred
.- 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 withpred
.- ...
Other arguments accepted by
sim_slopes
andinteract_plot
Details
This function simply merges the nearly-equivalent arguments needed to call
both sim_slopes
and interact_plot
without the
need for re-typing their common arguments. Note that each function is called
separately and they re-fit a separate model for each level of each
moderator; therefore, the runtime may be considerably longer than the
original model fit. For larger models, this is worth keeping in mind.
Sometimes, you may want different parameters when doing simple slopes analysis compared to when plotting interaction effects. For instance, it is often easier to interpret the regression output when variables are standardized; but plots are often easier to understand when the variables are in their original units of measure.
probe_interaction
does not
support providing different arguments to each function. If that is needed,
use sim_slopes
and interact_plot
directly.
Value
The sim_slopes
object created.
The ggplot
object created by
interact_plot
.
See Also
Other interaction tools: johnson_neyman
,
sim_margins
, sim_slopes
Examples
# NOT RUN {
# Using a fitted model as formula input
fiti <- lm(Income ~ Frost + Murder * Illiteracy,
data=as.data.frame(state.x77))
probe_interaction(model = fiti, pred = Murder, modx = Illiteracy,
modx.values = "plus-minus")
# 3-way interaction
fiti3 <- lm(Income ~ Frost * Murder * Illiteracy,
data=as.data.frame(state.x77))
probe_interaction(model = fiti3, pred = Murder, modx = Illiteracy,
mod2 = Frost, mod2.values = "plus-minus")
# 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 + sch.wide, design = dstrat)
probe_interaction(model = regmodel, pred = ell, modx = meals,
modx.values = "plus-minus", cond.int = TRUE)
# 3-way with survey and factor input
regmodel3 <- svyglm(api00 ~ ell * meals * sch.wide, design = dstrat)
probe_interaction(model = regmodel3, pred = ell, modx = meals,
mod2 = sch.wide)
# Can try different configurations of 1st vs 2nd mod
probe_interaction(model = regmodel3, pred = ell, modx = sch.wide,
mod2 = meals)
}
# }