Regions of significance for an interaction.
sig_regions calculates the Johnson-Neyman (J-N) regions of
significance for an interaction – the points at which the simple effect of
the categorical predictor changes from non-significant to significant.
sig_regions(model, ...) ## S3 method for class 'lm' sig_regions(model, alpha = 0.05, precision = 4, ...) ## S3 method for class 'glm' sig_regions(model, alpha = 0.05, precision = 4, ...)
model |
A fitted linear model of type 'lm' or 'glm' with one two-way interaction including one categorical predictor and one continuous variable. |
... |
Not currently implemented; used to ensure consistency with S3 generic. |
alpha |
The level at which to test for significance. Default value is .05. |
precision |
The number of decimal places to which to round the alpha level (e.g., precision=5 would look for regions of significance at .05000). |
This function takes a linear or generalized linear model with one two-way
interaction, where one of the predictors in the interaction is categorical
(factor) and the other is continuous. For other types of interaction terms,
use the simple_slopes function instead.
For more information about regions of significance, see Spiller, Fitzsimons, Lynch, & McClelland (2012).
A named vector with a 'lower' and an 'upper' J-N point. If one or more of the J-N points fall outside the range of your predictor, the function will return NA for that point. If your interaction is not significant, both J-N points will be NA.
lm: Johnson-Neyman points for linear models.
glm: Johnson-Neyman points for generalized linear models.
# mtcars data mtcars$am <- factor(mtcars$am) # make 'am' categorical model <- lm(mpg ~ wt * am, data=mtcars) summary(model) # significant interaction sig_regions(model)
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