plot_cco {marginaleffects} | R Documentation |
plot_comparisons()
is an alias to plot_comparisons()
Description
This alias is kept for backward compatibility.
Usage
plot_cco(
model,
variables = NULL,
condition = NULL,
by = NULL,
type = "response",
vcov = NULL,
conf_level = 0.95,
transform_pre = "difference",
transform_post = NULL,
rug = FALSE,
gray = FALSE,
draw = TRUE,
...
)
Arguments
model |
Model object
|
condition |
Conditional predictions
Character vector (max length 3): Names of the predictors to display.
Named list (max length 3): List names correspond to predictors. List elements can be:
Numeric vector
Function which returns a numeric vector or a set of unique categorical values
Shortcut strings for common reference values: "minmax", "quartile", "threenum"
1: x-axis. 2: color/shape. 3: facets.
Numeric variables in positions 2 and 3 are summarized by Tukey's five numbers ?stats::fivenum
|
by |
Marginal predictions
|
type |
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 type is NULL , the
default value is used. This default is the first model-related row in
the marginaleffects:::type_dictionary dataframe.
|
vcov |
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-consistent: "HC" , "HC0" , "HC1" , "HC2" , "HC3" , "HC4" , "HC4m" , "HC5" . See ?sandwich::vcovHC
Heteroskedasticity and autocorrelation consistent: "HAC"
Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger"
Other: "NeweyWest" , "KernHAC" , "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 sandwich::vcovCL function.
Square covariance matrix
Function which returns a covariance matrix (e.g., stats::vcov(model) )
|
conf_level |
numeric value between 0 and 1. Confidence level to use to build a confidence interval.
|
transform_post |
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.
|
rug |
TRUE displays tick marks on the axes to mark the distribution of raw data.
|
gray |
FALSE grayscale or color plot
|
draw |
TRUE returns a ggplot2 plot. FALSE returns a data.frame of 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
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
arguments.
|
Value
A ggplot2
object or data frame (if draw=FALSE
)
Examples
mod <- lm(mpg ~ hp + wt, data = mtcars)
plot_predictions(mod, condition = "wt")
mod <- lm(mpg ~ hp * wt * am, data = mtcars)
plot_predictions(mod, condition = c("hp", "wt"))
plot_predictions(mod, condition = list("hp", wt = "threenum"))
plot_predictions(mod, condition = list("hp", wt = range))
[Package
marginaleffects version 0.10.0
Index]