Scatter Plot for Interval-valued Data — plot_scatter

Create a scatter plot for interval-valued symbolic data, visualizing the symbolic data as rectangles or crosses, with the first two variables on the x and y axes. The function allows customization of colors, fill colors, and outlier representation.

Usage

plot_scatter_int(
  data,
  type = c("rectangles", "crosses", "crosses2"),
  palette = rainbow(nrow(data)),
  fill_col = "gray50",
  is_outlier = NULL,
  ...
)

Arguments

data: An intData object containing the macrodata/interval data. The first two variables are used for the x and y axes.
type: The type of plot to generate: "rectangles", "crosses" or "crosses2". Default is "rectangles".
palette: A vector with colors for each observation. Default is rainbow(nrow(data)).
fill_col: If type="rectangles", a vector with colors for the fill of each observation, or a single color for all observations. Default is "gray50".
is_outlier: A vector with logical values indicating if the observation is an outlier or not. It makes the line width of the outlying observations thicker. Default is NULL.
...: Additional graphical parameters.

Value

A scatter plot is drawn in the graphic window. The scatter plot shows the symbolic data as rectangles or crosses, with the first two variables on the x and y axes.

Examples

data(creditcard)
credit_card_int <- creditcard$intData

plot_scatter_int(credit_card_int[, c(3, 5)])


# Alternatively, highlight outliers in the scatter plot
# 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, "farness", 0.9)

outliers_colors <- rep('gray50', credit_card_int@NObs)
names(outliers_colors) <- rownames(credit_card_int)
outliers_colors[credit_card_outliers$outliers_names] = 'red'

plot_scatter_int(credit_card_int[, c(3, 5)], 
            palette = outliers_colors, 
            is_outlier = credit_card_outliers$is_outlier)