WeibullPDFAdstock

class pymc_marketing.mmm.components.adstock.WeibullPDFAdstock(l_max=FieldInfo(annotation=NoneType, required=True, description='Maximum lag for the adstock transformation.', metadata=[Gt(gt=0)]), normalize=FieldInfo(annotation=NoneType, required=False, default=True, description='Whether to normalize the adstock values.'), mode=FieldInfo(annotation=NoneType, required=False, default=<ConvMode.After: 'After'>, description='Convolution mode.'), priors=FieldInfo(annotation=NoneType, required=False, default=None, description='Priors for the parameters.'), prefix=FieldInfo(annotation=NoneType, required=False, default=None, description='Prefix for the parameters.'))

Wrapper around weibull adstock with PDF function.

For more information, see pymc_marketing.mmm.transformers.weibull_adstock().

(Source code, png, hires.png, pdf)

Methods

WeibullPDFAdstock.__init__([l_max, ...])
WeibullPDFAdstock.apply(x[, dims])	Call within a model context.
WeibullPDFAdstock.function(x, lam, k)	Weibull adstock function.
WeibullPDFAdstock.plot_curve(curve[, ...])	Plot curve HDI and samples.
WeibullPDFAdstock.plot_curve_hdi(curve[, ...])	Plot the HDI of the curve.
WeibullPDFAdstock.plot_curve_samples(curve)	Plot samples from the curve.
WeibullPDFAdstock.sample_curve(parameters[, ...])	Sample the adstock transformation given parameters.
WeibullPDFAdstock.sample_prior([coords])	Sample the priors for the transformation.
WeibullPDFAdstock.set_dims_for_all_priors(dims)	Set the dims for all priors.
WeibullPDFAdstock.to_dict()	Convert the adstock transformation to a dictionary.
WeibullPDFAdstock.update_priors(priors)	Update the priors for a function after initialization.

Attributes

combined_dims	Get the combined dims for all the parameters.
default_priors
function_priors	Get the priors for the function.
lookup_name
model_config	Mapping from variable name to prior for the model.
prefix
variable_mapping	Mapping from parameter name to variable name in the model.