probemod (version 0.2.1)

jn: Johnson-Neyman Technique

Description

Probe moderation effect using the Johnson-Neyman technique

Usage

jn(model, dv, iv, mod, mrange, alpha = 0.05, yas = "none")

Arguments

model

Regression model (lm, glm, list).

dv

Dependent variable (character).

iv

Independent variable (character).

mod

Moderator variable(s) (character or character vector).

mrange

Range of values that jn should examine for moderator variable. Uses the current range of moderator values by default (numeric vector).

alpha

Alpha level to use (numeric).

yas

Show y (or conditional effect) as: "none", "ratio","probability","percentage", yas="none" by default.

Value

• A list with the elements

References

Spiller, S. A., Fitzsimons, G. J., Lynch, J. G., Jr, & McClelland, G. H. (2013). Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression. Journal of Marketing Research, 50(2), 277-288.

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.

Examples

myModel <- lm('DV ~ IV + MOD', data=someData)
jnresults <- jn(myModel, dv='DV', iv='IV', mod='MOD')
jnresults <- jn(myModel, dv='DV', iv='IV', mod='MOD', alpha=.01)
plot(jnresults)