Function reference • vmc

Model check basis All vmc plots begin with a call to `mcplot()`, supplying the model object and observed data. You can then add components with `+`.
`mcplot()`	Create a new mcplot
`+`(<modelcheck>)	Add components to a plot
Distribution generated from the model The distributions generated from the model contain the predictive distribution and the distribution of push forward transformations that the model used to link the independent variables to the response variable.
`mc_draw()`	Define how to draw from posterior distribution
Uncertainty representation layers An uncertainty representation layers combines a geom (and the corresponding statistical transformation), the draws representation, and the sampling. You can specify different uncertainty representations for model distribution and observed data respectively by `mc_model_` and `mc_obs_`. You can also change the arguments of the ggplot/ggdist geoms by passing them in uncertainty representation layers.
`mc_obs_auto()` `mc_model_auto()`	Recommend a geom
`mc_obs_ccdf()` `mc_model_ccdf()`	CCDF bar plot for model predictions
`mc_obs_cdf()` `mc_model_cdf()`	CDF bar plot for model predictions
`mc_obs_custom()` `mc_model_custom()`	Customized geom
`mc_obs_dots()` `mc_model_dots()`	Dot plot for model predictions
`mc_obs_dotsinterval()` `mc_model_dotsinterval()`	Dots interval plot for model predictions
`mc_obs_eye()` `mc_model_eye()`	Eye (violin + interval) plot for model predictions
`mc_obs_gradientinterval()` `mc_model_gradientinterval()`	Gradient + interval plot for model predictions
`mc_obs_halfeye()` `mc_model_halfeye()`	Half-eye (density + interval) plot for model predictions
`mc_obs_histinterval()` `mc_model_histinterval()`	Histogram + interval plot for model predictions
`mc_obs_interval()` `mc_model_interval()`	Interval plot for model predictions
`mc_obs_line()` `mc_model_line()`	Line geom
`mc_obs_lineribbon()` `mc_model_lineribbon()`	Line + multiple-ribbon plot for model predictions
`mc_obs_point()` `mc_model_point()`	Points geom
`mc_obs_pointinterval()` `mc_model_pointinterval()`	Point + interval plot for model predictions
`mc_obs_ribbon()` `mc_model_ribbon()`	Multiple-ribbon plot for model predictions
`mc_obs_slab()` `mc_model_slab()`	Slab (ridge) plot for model predictions
`mc_obs_tile()` `mc_model_tile()`	Tile geom
`mc_obs_reference_line()` `mc_model_reference_line()`	Horizontal reference lines on y axis
Comparative layouts The layouts used to compare the model distribution and observed data in model check.
`mc_layout_juxtaposition()` `mc_layout_superposition()` `mc_layout_nested()` `mc_layout_encoding()`	Define comparative layout in model check visualization
Conditional variable The conditional check of the model enabled by conditioning variables on x axis, rows, and columns.
`mc_condition_on()`	Add marginal check to model check
Colors used in model check The color used by vmc to distinguish the visualization of model and the visualization of data.
`mc_color_palettes()` `mc_set_model_color()` `mc_set_obs_color()`	Colors in model check visualization
Transformation on observed data The transformation function to be applied on observed values of the response value.
`mc_observation_transformation()`	Define the transformation applied to observed data
Additional ggplot layers Additional ggplot layers to be added in the ggplot object that is generated by vmc.
`mc_gglayer()`	Add a `ggplot2::layer()` to model check visualization
Model vmc comes with a selection of built-in models that are used in examples to illustrate various model checks.
`mpg_model`	Miles/(US) gallon model

Reference

Model check basis

Distribution generated from the model

Uncertainty representation layers

Comparative layouts

Conditional variable

Colors used in model check

Transformation on observed data

Additional ggplot layers

Model