WeibullPDFAdstock#

class pymc_marketing.mmm.components.adstock.WeibullPDFAdstock(l_max=FieldInfo(annotation=int, required=True, description='Maximum lag for the adstock transformation.', metadata=[Gt(gt=0)]), normalize=FieldInfo(annotation=bool, required=False, default=True, description='Whether to normalize the adstock values.'), mode=FieldInfo(annotation=ConvMode, required=False, default=<ConvMode.After: 'After'>, description='Convolution mode.'), priors=FieldInfo(annotation=Union[dict[str, Union[Annotated[Prior, InstanceOf], float, Annotated[TensorVariable, InstanceOf], Annotated[VariableFactory, InstanceOf]]], NoneType], required=False, default=None, description='Priors for the parameters.'), prefix=FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Prefix for the parameters.'))[source]#

Wrapper around weibull adstock with PDF function.

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

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

../../_images/pymc_marketing-mmm-components-adstock-WeibullPDFAdstock-1.png

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.