Barplot of Shapley values for Interval-valued Data

Usage

plot_bar_int_Shapley(
  x,
  cutoff_value = NULL,
  cutoff_label = NULL,
  palette = NULL,
  abbrev.var = 20,
  abbrev.obs = 20,
  sort.obs = TRUE,
  plot_IMah = TRUE,
  IMah_label = expression(Robust ~ d[IMah]^2 * (bold(x))),
  rotate_x = TRUE
)

Arguments

x: A \(n \times p\) matrix containing the Shapley values of \(n\) observations and \(p\) variables.
cutoff_value: Numeric. The cutoff value used for detecting outliers. If cutoff_value is not NULL (default), the cutoff value is included in the plot.
cutoff_label: Character. Label for the cutoff value line in the plot.
palette: A vector with colors for each variable. If palette is NULL (default), the colors are generated using RColorBrewer.
abbrev.var: Integer. If abbrev.var \(> 0\), column names are abbreviated using abbreviate with minlenght = abrev.var.
abbrev.obs: Integer. If abbrev.obs \(> 0\), row names are abbreviated using abbreviate with minlenght = abrev.obs.
sort.obs: Logical. If TRUE (default), observations are sorted according to their squared (robust) Interval-Mahalanobis distance.
plot_IMah: Logical. If TRUE (default), the squared (robust) Interval-Mahalanobis distance will be included in the plot.
IMah_label: Character. Label for the Interval-Mahalanobis distance in the plot legend. Default is "Robust \(d_\mathrm{IMah}^2(\boldsymbol{x})\)".
rotate_x: Logical. If TRUE (default), the x-axis labels are rotated.

Value

Returns a barplot that displays the Shapley values (int_Shapley) for each observation and optionally (plot_IMah = TRUE) includes the squared (robust) Interval-Mahalanobis distance (IMah_dist) (black bar) and the corresponding outlier detection cut-off value (dotted line).

References

Adapted from package ShapleyOutlier (https://CRAN.R-project.org/package=ShapleyOutlier).

Examples

# Create intData object
data(creditcard)
credit_card_int <- creditcard$intData

# Estimate the mean and covariance matrix
credit_card_IMCD <- IMCD(credit_card_int, 
                         m = floor(nrow(credit_card_int)*0.75), 
                         cutoff = "farness", 
                         cutoff_lvl = 0.9)

# Detect outliers using farness cutoff
credit_card_outliers <- int_outliers(credit_card_IMCD$robust_dist, 
                                     cutoff = "farness", 
                                     cutoff_lvl = 0.9)

# Compute Shapley values
credit_card_shapley <- int_Shapley(credit_card_int, 
                                   mean_c = credit_card_IMCD$mean_IMCD_c, 
                                   mean_r = credit_card_IMCD$mean_IMCD_r, 
                                   cov = credit_card_IMCD$cov_IMCD)

# Plot Shapley values with cutoff line and Interval-Mahalanobis distance
plot_bar_int_Shapley(credit_card_shapley, 
                    cutoff_value = credit_card_outliers$cutoff_value,
                    cutoff_label = "Farness 0.9",
                    palette = rainbow(credit_card_int@NIVar))