Package 'EntropicStatistics'

Heat Map for Distribution Visualization

Description

Return a heat map that displays distributional characteristics for selected groups.

Usage

HeatMap(
  data_frequency_list,
  orders = seq(0.5, 3, by = 0.01),
  selection = 1:length(data_frequency_list),
  plot_order = selection,
  RowNames = names(data_frequency_list)[plot_order],
  title = "HeatMap",
  x_ticks = round(stats::quantile(orders, c(0, 0.25, 0.5, 0.75, 1)), 2),
  plot_margin = ggplot2::margin(0.5, 0.2, 0.2, 1, "cm"),
  text_face = 1,
  fill_colors = c("blue4", "white", "red3"),
  title_text_size = 25,
  label_text_size = 25
)

Arguments

data_frequency_list	A list of nonnegative frequency/count vectors (e.g., from table()). Element names (if present) are used as row labels.
orders	Numeric vector of generalized Shannon entropy orders to evaluate.
selection	Integer indices selecting which elements of data_frequency_list to include.
plot_order	Integer indices giving the order (bottom to top) of the selected groups.
RowNames	Character vector of row labels for the selected groups (default: names(data_frequency_list)[plot_order]).
title	Character string, plot title.
x_ticks	Numeric vector of x-axis tick locations; values must be present in orders.
plot_margin	Plot margin, a ggplot2::margin() object.
text_face	Integer font face in the plot: 1 = "plain", 2 = "italic", 3 = "bold", 4 = "bold.italic".
fill_colors	Character vector of three colors for low, mid, and high values.
title_text_size	Numeric size of the title text.
label_text_size	Numeric size of axis text.

Details

Provides a quick, nonparametric view of distributional differences across multiple groups simultaneously, using generalized Shannon entropy over a range of orders. Input vectors should be nonnegative counts. Zero-probability categories are handled internally.

Value

A ggplot object representing the heat map.

References

Zhang, J. and Shi, J. (2024). Nonparametric clustering of discrete probability distributions with generalized Shannon's entropy and heatmap. Statistics & Probability Letters. doi:10.1016/j.spl.2024.110070

See Also

ggplot, geom_tile

Examples

set.seed(1)
binom_n <- 10
sample_size <- 400
sample_1 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.1))
sample_2 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.2))
sample_3 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.3))
sample_4 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.4))
sample_5 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.5))
sample_6 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.6))
sample_7 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.7))
sample_8 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.8))
sample_9 <- table(stats::rbinom(n = sample_size, size = binom_n, prob = 0.9))
poisson_1 <- table(stats::rpois(n = sample_size, lambda = 1))
poisson_2 <- table(stats::rpois(n = sample_size, lambda = 2))
poisson_3 <- table(stats::rpois(n = sample_size, lambda = 3))
poisson_4 <- table(stats::rpois(n = sample_size, lambda = 4))
poisson_5 <- table(stats::rpois(n = sample_size, lambda = 5))
poisson_6 <- table(stats::rpois(n = sample_size, lambda = 6))
poisson_7 <- table(stats::rpois(n = sample_size, lambda = 7))
poisson_8 <- table(stats::rpois(n = sample_size, lambda = 8))
poisson_9 <- table(stats::rpois(n = sample_size, lambda = 9))
data_samples <- list(
  binom_0.1 = sample_1, binom_0.2 = sample_2, binom_0.3 = sample_3,
  binom_0.4 = sample_4, binom_0.5 = sample_5, binom_0.6 = sample_6,
  binom_0.7 = sample_7, binom_0.8 = sample_8, binom_0.9 = sample_9,
  Poisson_1 = poisson_1, Poisson_2 = poisson_2, Poisson_3 = poisson_3,
  Poisson_4 = poisson_4, Poisson_5 = poisson_5, Poisson_6 = poisson_6,
  Poisson_7 = poisson_7, Poisson_8 = poisson_8, Poisson_9 = poisson_9
)
HeatMap(data_samples)
HeatMap(data_samples, selection = sample(seq_along(data_samples), 6))
HeatMap(data_samples, selection = 1:9)
HeatMap(data_samples, selection = 10:13)
HeatMap(data_samples, selection = 14:18)

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