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Distance-Distance plot for interval-valued data.

Source: R/plots.r
plot_dist_dist.Rd

Distance-Distance plot for interval-valued data.

Usage

plot_dist_dist(
  class_dist,
  class_cutoff = NULL,
  class_cutoff_label = NULL,
  rob_dist,
  rob_cutoff = NULL,
  rob_cutoff_label = NULL,
  obs_names = NULL,
  ggplotly = FALSE,
  color_class = NULL,
  color_label = NULL,
  palette = NULL,
  shape_class = NULL,
  shape_label = NULL,
  label_obs = NULL
)

Arguments

class_dist

A numeric vector containing the classical distances for each observation.

class_cutoff

Numeric. The cutoff value for the classical distances.

class_cutoff_label

Character. Label for the classical cutoff. If NULL (default), no legend for the classical cutoff is shown.

rob_dist

A numeric vector containing the robust distances for each observation.

rob_cutoff

Numeric. The cutoff value for the robust distances.

rob_cutoff_label

Character. Label for the robust cutoff. If NULL (default), no legend for the robust cutoff is shown.

obs_names

A character vector containing the names of the observations. If NULL (default), the names are taken from the names of class_dist.

ggplotly

Logical. If TRUE (default), the plot is converted to an interactive plotly object.

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 when ggplotly = FALSE. Default is NULL.

Value

Returns a Distance-Distance plot that displays the classical distances against the robust distances for each observation, highlighting outliers.

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)

# Compute classical distances and outliers
class_dist <- IMah_dist(credit_card_int, z = rep(1,credit_card_int@NObs))
class_outliers <- int_outliers(class_dist, 
                               cutoff = "chi-squared", 
                               p = credit_card_int@NIVar)

# 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 Distance-Distance plot 
plot_dist_dist(class_dist, 
               class_cutoff = class_outliers$cutoff_value, 
               class_cutoff_label = "0.975 chi-squared",
               rob_dist = credit_card_dist, 
               rob_cutoff = credit_card_outliers$cutoff_value, 
               rob_cutoff_label = "0.9 farness",
               color_class = credit_card_is_outliers, 
               palette = c("grey50", "red"))