Interval-Mahalanobis distance plot for interval-valued data.

Usage

plot_interval_dist(
  dist,
  cutoff = NULL,
  cutoff_label = NULL,
  obs_names = NULL,
  sort.obs = TRUE,
  color_class = NULL,
  color_label = NULL,
  palette = NULL,
  shape_class = NULL,
  shape_label = NULL,
  label_obs = NULL
)

Arguments

dist: A numeric vector containing the Interval-Mahalanobis distances for each observation.
cutoff: A numeric vector containing cutoff values to be displayed as horizontal lines.
cutoff_label: A character vector containing labels for each cutoff. If NULL (default), default labels are generated.
obs_names: A character vector containing the names of the observations. If NULL (default), the names are taken from the names of dist.
sort.obs: Logical. If TRUE (default), observations are sorted according to their distances.
color_class: A vector indicating the color class of each observation. If NULL (default), all points have the same color.
color_label: Character. Label for the color class. If NULL (default), no legend for the color class is shown.
palette: A vector with colors for each color class. If NULL (default), default ggplot2 colors are used.
shape_class: A vector indicating the shape class of each observation. If NULL (default), all points have the same shape.
shape_label: Character. Label for the shape class. If NULL (default), no legend for the shape class is shown.
label_obs: A vector with the names of the observations to be labeled in the plot. If NULL (default), no labels are shown and x-axis labels are displayed.

Value

Returns a plot that displays the Interval-Mahalanobis distances for each observation, highlighting outliers based on specified cutoffs.

Examples

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

# Compute robust distances using IMCD estimates of mean and covariance
credit_card_dist <- IMah_dist(credit_card_int)

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

# Create a vector indicating if the observations are outliers or inliers 
# based on the robust distance outlier detection
credit_card_is_outliers <- as.character(credit_card_outliers$is_outlier)
credit_card_is_outliers[credit_card_outliers$is_outlier] <- "Outlier"
credit_card_is_outliers[!credit_card_outliers$is_outlier] <- "Inlier"

# Plot Interval-Mahalanobis distance plot
plot_interval_dist(credit_card_dist,
                   cutoff = credit_card_outliers$cutoff_value,
                   cutoff_label = c("0.9 farness"),
                   obs_names = rownames(credit_card_int),
                   sort.obs = FALSE,
                   color_class = credit_card_is_outliers,
                   palette = c("grey50", "red"))