ensModelVis (version 0.2.0)

plot_ensemble: Draws a plot for model predictions of ensembles of models. For classification the plot is a heatmap, for regression, scatterplot.

Description

Draws a plot for model predictions of ensembles of models. For classification the plot is a heatmap, for regression, scatterplot.

Usage

plot_ensemble(
  truth,
  tibble_pred,
  incorrect = FALSE,
  tibble_prob = NULL,
  order = NULL,
  facet = FALSE
)

Value

a ggplot

Arguments

truth

The y variable. In regression this is numeric vector, in classification this is a factor vector.

tibble_pred

A data.frame of predictions. Each column corresponds to a candidate model.

incorrect

If TRUE, for observations that were correctly classified by all models, remove all but a single observation per class. Classification only.

tibble_prob

If not NULL, a data.frame with same column names as tibble_pred. Applies transparency based on the predicted probability of the predicted class. Classification only.

order

default ordering of columns in a heatmap (classification) or facets (regression) is by accuracy (classification) or RMSE (regression). Can submit any other ordering for heatmaps e.g. AUC, which should be a data.frame with same column names as tibble_pred.

facet

whether to facet the plots by model (regression only).

Examples

data(iris)
if (require("MASS")){
lda.model <- lda(Species~., data = iris)
lda.pred <- predict(lda.model)
}
if (require("ranger")){
ranger.model <- ranger(Species~., data = iris)
ranger.pred <- predict(ranger.model, iris)
}

library(ensModelVis)

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

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

if (require("ranger")){
ranger.model <- ranger(Species~., data = iris, probability = TRUE)
ranger.prob <- predict(ranger.model, iris)
}

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