Visualising and Interpreting Statistical Models Fit to Compositional Data
Add additional variables to the data
Add identity effect groups used in a Diversity-Interactions (DI) model...
Add interaction terms used in a Diversity-Interactions (DI) model to n...
Add predictions and confidence interval to data using either a model o...
Conditional ternary diagrams
Conditional ternary diagrams
Conditional ternary diagrams
Copy attributes from one object to another
Special custom filtering for compositional data
DImodelsVis: Model interpretation and visualisation for compositional ...
Return colour-blind friendly colours
Get all equi-proportional communities at specific levels of richness
Returns shades of colours
Visualise change in (predicted) response over diversity gradient
Calculate change in predicted response over diversity gradient
Visualise change in (predicted) response over diversity gradient
Combine variable proportions into groups
Conditional ternary diagrams at functional group level
Grouped ternary diagrams
Conditional ternary diagrams at functional group level
Regression diagnostics plots with pie-glyphs
Visualising model selection
Model term contributions to predicted response
Model term contributions to predicted response
Visualise model term contributions to predicted response
Visualising the change in a response variable between two points in th...
Creating data for visualising the change in a response variable betwee...
Visualising the change in a response variable between two points in th...
Project 3-d compositional data onto x-y plane and vice versa
Prepare data for showing contours in ternary diagrams.
Ternary diagrams
Default theme for DImodelsVis
Effects plot for compositional data
Prepare data for effects plots for compositional data
Effects plot for compositional data
Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object.