`probe_interaction`

is a convenience function that allows users to call
both `sim_slopes`

and `interact_plot`

with a single
call.

## 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`

.- ...
Other arguments accepted by

`sim_slopes`

and`interact_plot`

## Value

- simslopes
The

`sim_slopes`

object created.- interactplot
The

`ggplot`

object created by`interact_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.

## See also

Other interaction tools:
`johnson_neyman()`

,
`sim_margins()`

,
`sim_slopes()`

## Author

Jacob Long jacob.long@sc.edu

## Examples

```
# 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")
#> Error in formula(model): object 'fiti' not found
# 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")
#> Error in formula(model): object 'fiti3' not found
# 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)
}
#> Error in formula(model): object 'regmodel' not found
```