Simple slopes in brms?

[Zachary Horne]

Hi,

I have a basic question about getting simple slopes from a model like this:

a = gender

b= likert scale 

c = phd or no phd

DV ~ a + b + c + a*b

I'm hoping to get the simple slopes from the interaction term in this model. Is there a straight forward way to do this in brms?

Thanks!

Zach

[Paul Buerkner]

You can easily vizualize the interaction via marginal_effects(fit, "b:a") or marginal_effects(fit, "a:b").

Is this what you have in mind?

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[Zachary Horne]

Hi Paul,

Thanks for the reply. I figured out how to visualize the effects without a problem using marginal effects. However, I want to be able to actually report the slope of the likert scale ratings predicting the DV for men and women separately. This is clearly being displayed in the marginal effects plot which makes me think there is a way to extract this information from whatever is creating that plot. 

On Tuesday, February 20, 2018 at 10:43:14 AM UTC-7, Paul Buerkner wrote:

You can easily vizualize the interaction via marginal_effects(fit, "b:a") or marginal_effects(fit, "a:b").

Is this what you have in mind?

2018-02-20 16:44 GMT+01:00 Zachary Horne <zach.s...@gmail.com>:

Hi,

I have a basic question about getting simple slopes from a model like this:

a = gender

b= likert scale 

c = phd or no phd

DV ~ a + b + c + a*b

I'm hoping to get the simple slopes from the interaction term in this model. Is there a straight forward way to do this in brms?

Thanks!

Zach

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[Paul Buerkner]

Suppose that a is dummy coded with male as the reference category.

Then (the coefficient) b is the slope for males, b + b:afemale is the slope for femals, and b:afemale is the difference of the slopes.

With exception of b + b:afemale, you can directly extract the coefficients from the summary output. For b + b:afemale, you write

hypothesis(fit, "b + b:afemale = 0")

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[Zachary Horne]

Hi Paul

This is really helpful. Thanks!

Zach