Polynomial Regression Plot — polynomial_regression

The function create a simple regression plot (no interaction). Can be used to visualize polynomial regression.

polynomial_regression_plot(
  model,
  model_data = NULL,
  predictor,
  graph_label_name = NULL,
  x_lim = NULL,
  y_lim = NULL,
  plot_color = FALSE
)

Arguments

model: object from lm
model_data: optional dataframe (in case data cannot be retrieved from the model)
predictor: predictor variable name (must be character)
graph_label_name: vector of length 3 or function. Vector should be passed in the form of c(response_var, predict_var1, predict_var2). Function should be passed as a switch function that return the label based on the name passed (e.g., a switch function)
x_lim: the plot's upper and lower limit for the x-axis. Length of 2. Example: c(lower_limit, upper_limit)
y_lim: the plot's upper and lower limit for the y-axis. Length of 2. Example: c(lower_limit, upper_limit)
plot_color: default if FALSE. Set to TRUE if you want to plot in color

Value

an object of class ggplot

Details

It appears that predict cannot handle categorical factors. All variables are converted to numeric before plotting.

Examples

fit = lm(data = iris, Sepal.Length ~ poly(Petal.Length,2))
polynomial_regression_plot(model = fit,predictor = 'Petal.Length')