Visualisations for Model Ensembles.

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)
  )
)