partial_residuals()
andbinned_residuals()
now reject tidyselect syntax that tries to rename predictors, or that results in no predictors being selected. This syntax already caused the functions to fail with strange error messages, so it is now more explicitly rejected.
This version fixes several bugs that arose during classroom use.
-
Simulation functions (
model_lineup()
,parametric_boot_distribution()
, andsampling_distribution()
) now check to determine if the model being simulated from was fit using thedata =
argument, and issue an error if it was not. The simulations work by callingupdate(fit, data = ...)
with newly simulated data, andupdate()
uses this to call the model fit function again with the specifieddata =
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 tocars
. 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 theerror_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 whenalternative_fit
uses predictors that were not used infit
. Previously, these would fail because the simulated data frame only contained the predictors used infit
. Supply the newdata
argument to specify the data frame used in simulations.
First version released to CRAN.