predictions

predictions

Functions

Name	Description
avg_predictions	Predict average outcomes (TO DO)
predictions	Predict outcomes using a fitted model on a specified scale for given combinations of values

avg_predictions

predictions.avg_predictions(
    model,
    conf_level=0.95,
    vcov=True,
    by=True,
    newdata=None,
    hypothesis=None,
    equivalence=None,
    transform=None,
    wts=None,
)

Predict average outcomes (TO DO)

predictions

predictions.predictions(
    model,
    variables=None,
    conf_level=0.95,
    vcov=True,
    by=False,
    newdata=None,
    hypothesis=None,
    equivalence=None,
    transform=None,
    wts=None,
    eps_vcov=None,
)

Predict outcomes using a fitted model on a specified scale for given combinations of values of predictor variables, such as their observed values, means, or factor levels (reference grid).

This function handles unit-level (conditional) estimates and average (marginal) estimates based on the variables and newdata arguments. See the package website and vignette for examples: - https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html - https://vincentarelbundock.github.io/marginaleffects/

Parameters

Name	Type	Description	Default
model	object	Model object.	required
newdata	Union[None, DataFrame]	Grid of predictor values at which to evaluate predictions, by default predictions are made on the data used to fit the model.	None
by			False
wts			None
transform		- transform = numpy.exp - transform = lambda x: x.exp() - transform = lambda x: x.map_elements()	None
hypothesis			None

Returns

Name	Type	Description
	DataFrame	A DataFrame with one row per observation and several columns: - rowid: row number of the newdata data frame - type: prediction type, as defined by the type argument - group: (optional) value of the grouped outcome (e.g., categorical outcome models) - estimate: predicted outcome - std_error: standard errors computed using the delta method. - p_value: p value associated with the estimate column. - s_value: Shannon information transforms of p values. - conf_low: lower bound of the confidence interval (or equal-tailed interval for Bayesian models) - conf_high: upper bound of the confidence interval (or equal-tailed interval for Bayesian models)