Plot predictions on the y-axis against values of one or more predictors (x-axis, colors/shapes, and facets).
The by argument is used to plot marginal predictions, that is, predictions made on the original data, but averaged by subgroups. This is analogous to using the by argument in the predictions() function.
The condition argument is used to plot conditional predictions, that is, predictions made on a user-specified grid. This is analogous to using the newdata argument and datagrid() function in a predictions() call.
All unspecified variables are held at their mean or mode. This includes grouping variables in mixed-effects models, so analysts who fit such models may want to specify the groups of interest using the variables argument, or supply model-specific arguments to compute population-level estimates. See details below.
See the "Plots" vignette and website for tutorials and information on how to customize plots:
https://vincentarelbundock.github.io/marginaleffects/articles/plot.html
https://vincentarelbundock.github.io/marginaleffects
plot_predictions(
model,
condition = NULL,
by = NULL,
newdata = NULL,
type = NULL,
vcov = NULL,
conf_level = 0.95,
wts = NULL,
transform = NULL,
points = 0,
rug = FALSE,
gray = FALSE,
draw = TRUE,
...
)A ggplot2 object or data frame (if draw=FALSE)
Model object
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
Marginal predictions
Character vector (max length 3): Names of the categorical predictors to marginalize across.
1: x-axis. 2: color. 3: facets.
When newdata is NULL, the grid is determined by the condition argument. When newdata is not NULL, the argument behaves in the same way as in the predictions() function.
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.
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))
numeric value between 0 and 1. Confidence level to use to build a confidence interval.
string or numeric: weights to use when computing average
contrasts or slopes. These weights only affect the averaging in
avg_*() or with the by argument, and not the unit-level estimates themselves.
string: column name of the weights variable in newdata. When supplying a column name to wts, it is recommended to supply the original data (including the weights variable) explicitly to newdata.
numeric: vector of length equal to the number of rows in the original data or in newdata (if supplied).
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.
Number between 0 and 1 which controls the transparency of raw data points. 0 (default) does not display any points.
TRUE displays tick marks on the axes to mark the distribution of raw data.
FALSE grayscale or color plot
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.
Some model types allow model-specific arguments to modify the nature of
marginal effects, predictions, marginal means, and contrasts. Please report
other package-specific predict() arguments on Github so we can add them to
the table below.
https://github.com/vincentarelbundock/marginaleffects/issues
| Package | Class | Argument | Documentation |
brms | brmsfit | ndraws | brms::posterior_predict |
re_formula | brms::posterior_predict | ||
lme4 | merMod | re.form | lme4::predict.merMod |
allow.new.levels | lme4::predict.merMod | ||
glmmTMB | glmmTMB | re.form | glmmTMB::predict.glmmTMB |
allow.new.levels | glmmTMB::predict.glmmTMB | ||
zitype | glmmTMB::predict.glmmTMB | ||
mgcv | bam | exclude | mgcv::predict.bam |
robustlmm | rlmerMod | re.form | robustlmm::predict.rlmerMod |
allow.new.levels | robustlmm::predict.rlmerMod | ||
MCMCglmm | MCMCglmm | ndraws |
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))
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