# Heat Map for Distribution Visualization Return a heat map that displays distributional characteristics for selected groups. ```r 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. ## Returns A `ggplot` object representing the heat map. ## 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. ## Examples ```r 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) ``` ## References Zhang, J. and Shi, J. (2024). Nonparametric clustering of discrete probability distributions with generalized Shannon's entropy and heatmap. **Statistics & Probability Letters**. tools:::Rd_expr_doi("10.1016/j.spl.2024.110070") ## See Also `ggplot`, `geom_tile`

HeatMap function