This version fixes several bugs that arose during classroom use.
Simulation functions (model_lineup(), parametric_boot_distribution(), and sampling_distribution()) now check to determine if the model being simulated from was fit using the data = argument, and issue an error if it was not. The simulations work by calling update(fit, data = ...) with newly simulated data, and update() uses this to call the model fit function again with the specified data = argument. But if the model was fit without one, the argument is unused, and the simulations just reuse the original data.
For example, if you fit this model:
bad_fit <- lm(cars$dist ~ cars$speed)the simulation functions cannot work correctly because even with a different data = argument, the model fit will still refer to cars. The model should be fit like this:
good_fit <- lm(dist ~ speed, data = cars)To prevent simulation problems, a suitable error is issued, so the user can refit the model correctly.
response() now correctly detects when the error_scale argument was missing and issues the appropriate error.
augment_longer() now supports models with factor predictors. If there are some factors and some continuous predictors, the factors are omitted from the result; if the predictors are all factors, they are kept.
parametric_boot_distribution() now supports simulations when alternative_fit uses predictors that were not used in fit. Previously, these would fail because the simulated data frame only contained the predictors used in fit. Supply the new data argument to specify the data frame used in simulations.
First version released to CRAN.