ggeffects (version 1.3.1)

johnson_neyman: Spotlight-analysis: Create Johnson-Neyman confidence intervals and plots

Description

Function conduct a spotlight-analysis to create so-called Johnson-Neyman intervals. The plot() method can be used to visualize the results of the Johnson-Neyman test.

Usage

johnson_neyman(x, precision = 500, ...)
spotlight_analysis(x, precision = 500, ...)
# S3 method for ggjohnson_neyman
plot(
  x,
  colors = c("#f44336", "#2196F3"),
  show_association = TRUE,
  show_rug = FALSE,
  verbose = TRUE,
  ...
)

Value

A Johnson-Neyman plot.

Arguments

x

An object of class ggeffects, as returned by the functions from this package.

precision

Number of values used for the range of the moderator variable to calculate the Johnson-Neyman interval. This argument is passed down to pretty(..., n = precision). Usually, the default value of 500 is sufficient. Increasing this value will result in a smoother plot and more accurate values for the interval bounds, but can also slightly increase the computation time.

...

Arguments passed down to hypothesis_test() (and then probably further to marginaleffects::slopes()).

colors

Colors used for the plot. Must be a vector with two color values. Only used if show_association = TRUE.

show_association

Logical, if TRUE, highlights the range where values of the moderator are positively or negtatively associated with the outcome.

show_rug

Logical, if TRUE, adds a rug with raw data of the moderator variable to the plot. This helps visualizing its distribution.

verbose

Show/hide printed message for plots.

Details

The Johnson-Neyman intervals help to understand where slopes are significant in the context of interactions in regression models. Thus, the interval is only useful if the model contains at least one interaction term. The function accepts the results of a call to ggpredict(), ggeffect() or ggemmeans(). The first and the last focal term used in the terms argument of ggpredict() etc. must be numeric. The function will then test the slopes of the first focal terms against zero, for different moderator values of the last focal term. Use plot() to create a plot of the results.

To avoid misleading interpretations of the plot, we speak of "positive" and "negative" associations, respectively, and "no clear" associations (instead of "significant" or "non-significant"). This should prevent the user from considering a non-significant range of values of the moderator as "accepting the null hypothesis".

References

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. doi: 10.1207/s15327906mbr4003_5

Esarey, J., & Sumner, J. L. (2017). Marginal effects in interaction models: Determining and controlling the false positive rate. Comparative Political Studies, 1–33. Advance online publication. doi: 10.1177/0010414017730080

Johnson, P.O. & Fay, L.C. (1950). The Johnson-Neyman technique, its theory and application. Psychometrika, 15, 349-367. doi: 10.1007/BF02288864

McCabe CJ, Kim DS, King KM. Improving Present Practices in the Visual Display of Interactions. Advances in Methods and Practices in Psychological Science. 2018;1(2):147-165. doi:10.1177/2515245917746792

Spiller, S. A., Fitzsimons, G. J., Lynch, J. G., & 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. doi:10.1509/jmr.12.0420

Examples

if (FALSE) {
data(efc)
efc$c172code <- as.factor(efc$c172code)
m <- lm(neg_c_7 ~ c12hour * barthtot * c172code, data = efc)

if (requireNamespace("ggplot2") && requireNamespace("marginaleffects")) {
  pr <- ggpredict(m, c("c12hour", "barthtot"))
  johnson_neyman(pr)
  plot(johnson_neyman(pr))

  pr <- ggpredict(m, c("c12hour", "c172code", "barthtot"))
  johnson_neyman(pr)
  plot(johnson_neyman(pr))

  # robust standard errors
  if (requireNamespace("sandwich")) {
    johnson_neyman(pr, vcov = sandwich::vcovHC)
  }
}
}