breakDown (version 0.2.2)

plot.broken: Break Down Plot

Description

Break Down Plot

Usage

# S3 method for broken
plot(
  x,
  trans = I,
  ...,
  top_features = 0,
  min_delta = 0,
  add_contributions = TRUE,
  vcolors = c(`-1` = "#f05a71", `0` = "#371ea3", `1` = "#8bdcbe", X = "#371ea3"),
  digits = 3,
  rounding_function = round,
  plot_distributions = FALSE
)

Value

a ggplot2 object

Arguments

x

the model model of 'broken' class

trans

transformation that shal be applied to scores

...

other parameters

top_features

maximal number of variables from model we want to plot

min_delta

minimal stroke value of variables from model we want to plot

add_contributions

shall variable contributions to be added on plot?

vcolors

named vector with colors

digits

number of decimal places (round) or significant digits (signif) to be used. See the rounding_function argument

rounding_function

function that is to used for rounding numbers. It may be signif() which keeps a specified number of significant digits. Or the default round() to have the same precision for all components

plot_distributions

if TRUE then distributions of conditional propotions will be plotted. This requires keep_distributions=TRUE in the broken.default().

Examples

if (FALSE) {
library("breakDown")
library("randomForest")
library("ggplot2")
set.seed(1313)
model <- randomForest(factor(left)~., data = HR_data, family = "binomial", maxnodes = 5)
predict.function <- function(model, new_observation)
      predict(model, new_observation, type="prob")[,2]
predict.function(model, HR_data[11,-7])
explain_1 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
predict.function = predict.function, direction = "down")
explain_1
plot(explain_1) + ggtitle("breakDown plot (direction=down) for randomForest model")

explain_2 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
predict.function = predict.function, direction = "down", keep_distributions = TRUE)
plot(explain_2, plot_distributions = TRUE) +
         ggtitle("breakDown distributions (direction=down) for randomForest model")

explain_3 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
predict.function = predict.function, direction = "up", keep_distributions = TRUE)
plot(explain_3, plot_distributions = TRUE) +
         ggtitle("breakDown distributions (direction=up) for randomForest model")

model <- lm(quality~., data=wine)
new_observation <- wine[1,]
br <- broken(model, new_observation)
plot(br)
plot(br, top_features = 2)
plot(br, top_features = 2, min_delta = 0.01)
}