R: Break Down Plot

plot.broken {breakDown} | R Documentation

## Break Down Plot

### Description

Break Down Plot

### Usage

    ## S3 method for class 'broken'
    plot(x, trans = I, ..., top_features = 0, min_delta = 0,
      add_contributions = TRUE, vcolors = c(`-1` = "#d8b365", `0` = "#f5f5f5",
      `1` = "#5ab4ac", X = "darkgrey"), digits = 3, rounding_function = round,
      plot_distributions = FALSE)

### 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().

### Value

a ggplot2 object

### Examples

    ## Not run:
    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)

    ## End(Not run)

[Package breakDown version 0.1.6 Index]