LogSaturation

class pymc_marketing.mmm.components.saturation.LogSaturation(priors=None, prefix=None)

Logarithmic saturation for log-log models.

Applies \(\beta \, \log(1 + x)\), mapping spend through a concave logarithmic curve with diminishing returns. When combined with link="log" in the MMM, the coefficient \(\beta\) has an elasticity-like interpretation.

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

Methods

LogSaturation.__init__([priors, prefix])
LogSaturation.apply(x, *[, dims, core_dim, idx])	Call within a model context.
LogSaturation.from_dict(data)	Reconstruct a saturation transformation from a dict.
LogSaturation.function(x, beta, *[, dim])	Logarithmic saturation function: beta * log(1 + x).
LogSaturation.plot_curve(curve[, n_samples, ...])	Plot curve HDI and samples.
LogSaturation.plot_curve_hdi(curve[, ...])	Plot the HDI of the curve.
LogSaturation.plot_curve_samples(curve[, n, ...])	Plot samples from the curve.
LogSaturation.sample_curve([parameters, ...])	Sample the curve of the saturation transformation given parameters.
LogSaturation.sample_prior([coords])	Sample the priors for the transformation.
LogSaturation.set_dims_for_all_priors(dims)	Set the dims for all priors.
LogSaturation.to_dict([_orig])	Convert the transformation to a dictionary.
LogSaturation.update_priors(priors)	Update the priors for a function after initialization.
LogSaturation.with_default_prior_dims(dims)	Return a copy with default prior dims (dims=None) set to dims instead.
LogSaturation.with_updated_priors(priors)	Return a copy with updated priors.

Attributes

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