plot_slopes() is an alias to
This alias is kept for backward compatibility.
plot_cme( x, effect = NULL, condition = NULL, type = "response", vcov = NULL, conf_level = 0.95, draw = TRUE, ... )
character vector or named list of length smaller than 4. Character vectors must be the names of the predictor variables to display. The names of the list must The first element is displayed on the x-axis. The second element determines the colors. The third element creates facets. Other variables are held at their means or modes. Lists can include these types of values:
Function which returns a numeric vector or a set of unique categorical values
Shortcut strings for common reference values: "minmax", "quartile", "threenum"
string indicates the type (scale) of the predictions used to compute contrasts or slopes. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message. When
NULL, the default value is used. This default is the first model-related row in the
Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values:
FALSE: Do not compute standard errors. This can speed up computation considerably.
TRUE: Unit-level standard errors using the default
String which indicates the kind of uncertainty estimates to return.
Heteroskedasticity and autocorrelation consistent:
Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger"
"OPG". See the
One-sided formula which indicates the name of cluster variables (e.g.,
~unit_id). This formula is passed to the
clusterargument of the
Square covariance matrix
Function which returns a covariance matrix (e.g.,
numeric value between 0 and 1. Confidence level to use to build a confidence interval.
data.frameof the underlying data.
Additional arguments are passed to the
predict()method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the
marginaleffectswebsite). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the
?marginaleffectsdocumentation for a non-exhaustive list of available arguments.