ggeffects (version 1.7.1)

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

Value

A data frame including contrasts of the test_predictions() for the given interaction terms; for plot(), returns 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.

p_adjust

Character vector, if not NULL, indicates the method to adjust p-values. See stats::p.adjust() or stats::p.adjust.methods for details. Further possible adjustment methods are "tukey" or "sidak", and for johnson_neyman(), "fdr" (or "bh") and "esarey" (or its short-cut "es") are available options. Some caution is necessary when adjusting p-value for multiple comparisons. See also section P-value adjustment below.

...

Arguments passed down to test_predictions() (and then probably further to marginaleffects::slopes()). See ?test_predictions for further details.

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.

P-value adjustment for multiple comparisons

Note that p-value adjustment for methods supported by p.adjust() (see also p.adjust.methods), each row is considered as one set of comparisons, no matter which test was specified. That is, for instance, when test_predictions() returns eight rows of predictions (when test = NULL), and p_adjust = "bonferroni", the p-values are adjusted in the same way as if we had a test of pairwise comparisons (test = "pairwise") where eight rows of comparisons are returned. For methods "tukey" or "sidak", a rank adjustment is done based on the number of combinations of levels from the focal predictors in terms. Thus, the latter two methods may be useful for certain tests only, in particular pairwise comparisons.

For johnson_neyman(), the only available adjustment methods are "fdr" (or "bh") (Benjamini & Hochberg (1995)) and "esarey" (or "es") (Esarey and Sumner 2017). These usually return similar results. The major difference is that "fdr" can be slightly faster and more stable in edge cases, however, confidence intervals are not updated. Only the p-values are adjusted. "esarey" is slower, but confidence intervals are updated as well.

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 predict_response(). The first and the last focal term used in the terms argument of predict_response() 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. If only one numeric focal term is given, the function will create contrasts by levels of the categorical 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) { # requireNamespace("ggplot2") && requireNamespace("marginaleffects")
if (FALSE) {
data(efc, package = "ggeffects")
efc$c172code <- as.factor(efc$c172code)
m <- lm(neg_c_7 ~ c12hour * barthtot * c172code, data = efc)

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

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

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