This function plots adjusted predictions (y-axis) against values of one or more predictors (x-axis and colors).
plot_cap( model, condition = NULL, type = "response", vcov = NULL, conf_level = 0.95, transform_post = NULL, draw = TRUE, ... )
String or vector of two strings. The first is a variable name to be displayed on the x-axis. The second is a variable whose values will be displayed in different colors.
string indicates the type (scale) of the predictions used to compute marginal effects or contrasts. 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.
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
vcov(model) variance-covariance matrix.
String which indicates the kind of uncertainty estimates to return.
Heteroskedasticity and autocorrelation consistent:
"OPG". See the
sandwich package documentation.
One-sided formula which indicates the name of cluster variables (e.g.,
~unit_id). This formula is passed to the
cluster argument 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.
(experimental) A function applied to unit-level adjusted predictions and confidence intervals just before the function returns results. For bayesian models, this function is applied to individual draws from the posterior distribution, before computing summaries.
TRUE returns a
FALSE returns a
data.frame of the underlying data.
Additional arguments are passed to the
supplied by the modeling package.These arguments are particularly useful
for mixed-effects or bayesian models (see the online vignettes on the
marginaleffects website). 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
?marginaleffects documentation for a non-exhaustive list of available