get_model_matrix: Get a named model matrix

In marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests

Get a named model matrix

Description

Get a named model matrix

Usage

get_model_matrix(model, newdata)

## Default S3 method:
get_model_matrix(model, newdata)

Arguments

model

Model object

newdata

Grid of predictor values at which we evaluate the slopes. Warning: Please avoid modifying your dataset between fitting the model and calling a marginaleffects function. This can sometimes lead to unexpected results. NULL (default): Unit-level slopes for each observed value in the dataset (empirical distribution). The dataset is retrieved using insight::get_data(), which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model. datagrid() call to specify a custom grid of regressors. For example: newdata = datagrid(cyl = c(4, 6)): cyl variable equal to 4 and 6 and other regressors fixed at their means or modes. See the Examples section and the datagrid() documentation. subset() call with a single argument to select a subset of the dataset used to fit the model, ex: newdata = subset(treatment == 1) dplyr::filter() call with a single argument to select a subset of the dataset used to fit the model, ex: newdata = filter(treatment == 1) string: "mean": Slopes evaluated when each predictor is held at its mean or mode. "median": Slopes evaluated when each predictor is held at its median or mode. "balanced": Slopes evaluated on a balanced grid with every combination of categories and numeric variables held at their means. "tukey": Slopes evaluated at Tukey's 5 numbers. "grid": Slopes evaluated on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors).

marginaleffects documentation built on Oct. 5, 2024, 5:06 p.m.