Slope Estimate at Varying Level of Moderators — simple

The function uses the interaction::sim_slopes() to calculate the slope estimate at varying level of moderators (+/- 1 SD and mean). Additionally, it will produce a Johnson-Newman plot that shows when the slope estimate is not significant

simple_slope(model, data = NULL)

Arguments

model: model object from lm, lme,lmer
data: data.frame

Value

a list with the slope estimate data frame and a Johnson-Newman plot.

Examples

fit <- lm_model(
  data = iris,
  response_variable = Sepal.Length,
  predictor_variable = dplyr::everything(),
  three_way_interaction_factor = c(Sepal.Width, Petal.Width, Petal.Length)
)
#> Fitting Model with lm:
#>  Formula = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species + Sepal.Width*Petal.Width*Petal.Length

simple_slope_fit <- simple_slope(
  model = fit,
)
#> Warning: Multiple components found; returning the first one. To return all, use `return_all = TRUE`.