Package index • midr

Fit MID models

interpret(): Fit MID Models

Methods

plot(<mid>): Plot MID Component Functions

ggmid() autoplot(<mid>): Plot MID Component Functions with ggplot2

mid.plots(): Plot Multiple MID Component Functions

predict(<mid>): Predict Method for fitted MID Models

mid.effect() mid.f(): Evaluate Single MID Component Functions

print(<mid>): Print MID Models

summary(<mid>): Summarize MID Models

mid.terms(): Extract Terms from MID Models

Importance of effects

mid.importance(): Calculate MID Importance

Methods

plot(<mid.importance>): Plot MID Importance

ggmid(<mid.importance>) autoplot(<mid.importance>): Plot MID Importance with ggplot2

Breakdown of predictions

mid.breakdown(): Calculate MID Breakdowns

Methods

plot(<mid.breakdown>): Plot MID Breakdowns

ggmid(<mid.breakdown>) autoplot(<mid.breakdown>): Plot MID Breakdowns with ggplot2

Conditional expectations

mid.conditional(): Calculate MID Conditional Expectations

Methods

plot(<mid.conditional>): Plot MID Conditional Expectations

ggmid(<mid.conditional>) autoplot(<mid.conditional>): Plot MID Conditional Expectations with ggplot2

Other functions

Prediction Function

get.yhat(): Wrapper Prediction Function

Variable encoders

factor.encoder() factor.frame(): Encoder for Qualitative Variables

numeric.encoder() numeric.frame(): Encoder for Quantitative Variables

Color themes

color.theme(): Color Themes for Graphics

color.theme.info() color.theme.env(): Retrieve Color Theme Information

set.color.theme(): Register Color Themes

scale_color_theme() scale_colour_theme() scale_fill_theme(): Color Theme Scales for ggplot2 Graphics

Miscellaneous

theme_midr() par.midr(): Default Plotting Themes

weighted.loss(): Weighted Loss Function

shapviz(<mid>): Calculate MID-Derived Shapley Values