Default linear fitter for carryover tuning

A bare intercept-and-slope least squares fit of y on transformed media, supplied so that tune_carryover() has a sensible default and the simple case stays one line. It exists to be replaced: pass your own fit_fn and predict_fn to tune carryover against the model you actually intend to fit.

Usage

fit_ols(x, y)

# S3 method for class 'mm_ols'
predict(object, newdata, ...)

# S3 method for class 'mm_ols'
print(x, ...)

Arguments

x: For fit_ols(), a numeric vector of transformed (adstocked) media. For print(), an mm_ols object.
y: Numeric vector of the response, the same length as x.
object: An mm_ols object.
newdata: Numeric vector of transformed media to predict from.
...: Ignored, present for generic consistency.

Value

fit_ols() returns an object of class mm_ols: a list with elements intercept, slope and n. predict() returns a numeric vector the same length as newdata.

Details

This is deliberately the simplest possible model. It has no seasonality, no price term, no baseline and no controls, so carryover parameters chosen against it absorb whatever those omitted terms would have explained. That is fine for a first pass and wrong for a deliverable.

To tune against a model with controls, pass them to tune_carryover()'s controls argument, which switches the default model to least squares on the adstocked media plus the controls and keeps every row aligned with every split. For several channels at once use tune_carryover_joint(), and for carryover tuned jointly with saturation and a model penalty, step_adstock() in a recipes pipeline. All three are worked through in vignette("carryover") and vignette("tidymodels").

Examples