Visualisations for Model Ensembles

Displays for model fits of multiple models and their ensembles. For classification models, the plots are heatmaps, for regression, scatterplots.

ensModelVis

The goal of ensModelVis is to display model fits for multiple models and their ensembles.

Installation

You can install the development version of ensModelVis from GitHub with:

# install.packages("devtools")
devtools::install_github("domijan/ensModelVis")

Example

This is a basic example:

library(ensModelVis)

data(iris)
if (require("MASS")) {
  lda.model <- lda(Species ~ ., data = iris)
  lda.pred <- predict(lda.model)
}
#> Loading required package: MASS
if (require("ranger")) {
  ranger.model <- ranger(Species ~ ., data = iris, mtry = 1)
  ranger.pred <- predict(ranger.model, iris)
  ranger.model2 <-
    ranger(Species ~ .,
           data = iris,
           mtry = 4,
           num.trees = 10)
  ranger.pred2 <- predict(ranger.model2, iris)
}
#> Loading required package: ranger


plot_ensemble(
  iris$Species,
  data.frame(
    LDA = lda.pred$class,
    RF = ranger.pred$predictions,
    RF2 = ranger.pred2$predictions
  )
)

plot_ensemble(
  iris$Species,
  data.frame(
    LDA = lda.pred$class,
    RF = ranger.pred$predictions,
    RF2 = ranger.pred2$predictions
  ),
  incorrect = TRUE
)

if (require("ranger")) {
  ranger.model <- ranger(Species ~ ., data = iris, mtry = 1, probability = TRUE)
  ranger.prob <- predict(ranger.model, iris)
   ranger.model2 <-
    ranger(Species ~ .,
           data = iris,
           mtry = 4,
           num.trees = 10,
           probability = TRUE)
  ranger.prob2 <- predict(ranger.model2, iris)
}

plot_ensemble(
  iris$Species,
  data.frame(LDA = lda.pred$class,
             RF = ranger.pred$predictions,
             RF2 = ranger.pred2$predictions),
  tibble_prob = data.frame(
    LDA = apply(lda.pred$posterior, 1, max),
    RF = apply(ranger.prob$predictions, 1, max),
    RF2 = apply(ranger.prob2$predictions, 1, max)
  )
)