jn: Johnson-Neyman Technique

In probemod: Statistical Tools for Probing Moderation Effects

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

## Not run: 
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)

## End(Not run)

probemod documentation built on May 1, 2019, 10:11 p.m.