predictions: Adjusted Predictions

In marginaleffects: Marginal Effects, Marginal Means, Predictions, and Contrasts

Description

Compute model-adjusted predictions (fitted values) for a "grid" of regressor values.

Usage

predictions(
  model,
  variables = NULL,
  newdata = NULL,
  conf.level = 0.95,
  type = "response",
  ...
)

Arguments

model	Model object
variables	Character vector. Compute Adjusted Predictions for combinations of each of these variables. Factor levels are considered at each of their levels. Numeric variables variables are considered at Tukey's Five-Number Summaries. NULL uses the original data used to fit the model.
newdata	A dataset over which to compute adjusted predictions. NULL uses the original data used to fit the model.
conf.level	The confidence level to use for the confidence interval. No interval is computed if conf.int=NULL. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
type	Type(s) of prediction as string or vector This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero".
...	Additional arguments are pushed forward to predict().

Value

A data.frame with a predicted column with predictions.

marginaleffects documentation built on Oct. 19, 2021, 1:09 a.m.