| Type: | Package |
| Title: | Functions Based on Entropic Statistics |
| Version: | 0.1.0 |
| Maintainer: | Jialin Zhang (JZ) <jzhang@math.msstate.edu> |
| Description: | Contains methods for data analysis in entropic perspective. These entropic perspective methods are nonparametric, and perform better on non-ordinal data. Currently, the package has a function HeatMap() for visualizing distributional characteristics among multiple populations (groups). |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | ggplot2, ggrepel, hrbrthemes, tidyverse, dplyr, tidyr, tibble |
| NeedsCompilation: | no |
| RoxygenNote: | 7.2.3 |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Packaged: | 2023-05-26 19:09:31 UTC; jz505 |
| Author: | Jialin Zhang (JZ) [aut, cph, cre] |
| Repository: | CRAN |
| Date/Publication: | 2023-05-29 17:40:02 UTC |
HeatMap for Distribution Visualization
Description
Returns a heatmap to display characteristic information from selected groups.
Usage
HeatMap(
data_frequency_list,
orders = seq(0.50, 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 = 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 contains the frequency of data. Each sublist herein is a frequency counts of a group. |
orders |
Orders of Generalized Shannon's Entropy used in the heatmap. |
selection |
Indexes of sublist in |
plot_order |
The order of selected groups in the heatmap, from bottom to top. |
RowNames |
The display names of the selected groups in the heatmap. |
title |
The title of the heatmap. |
x_ticks |
The location of x-axis ticks on the heatmap. |
plot_margin |
The plot margins of the final heatmap. |
text_face |
The text style in the heatmap. |
fill_colors |
Three colors in the heatmap that represent lower, medium, and upper values. |
title_text_size |
Title text size in the heatmap. |
label_text_size |
Labels text size in the heatmap. |
Details
This is a preliminary tool to identify distributional information from multiple groups simultaneuously without any parametric assumptions.
Value
A heatmap plot made with ggplot2.
Author(s)
Jialin Zhang (JZ) at jzhang at math.msstate.edu.
Examples
## Creating data
binom_n <- 10
sample_size <- 1000
sample_1 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.1))
sample_2 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.2))
sample_3 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.3))
sample_4 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.4))
sample_5 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.5))
sample_6 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.6))
sample_7 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.7))
sample_8 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.8))
sample_9 <- table(stats::rbinom(size=binom_n, n=sample_size, 0.9))
sample_poisson_1 <- stats::rpois(sample_size, 1)
sample_poisson_2 <- stats::rpois(sample_size, 2)
sample_poisson_3 <- stats::rpois(sample_size, 3)
sample_poisson_4 <- stats::rpois(sample_size, 4)
sample_poisson_5 <- stats::rpois(sample_size, 5)
sample_poisson_6 <- stats::rpois(sample_size, 6)
sample_poisson_7 <- stats::rpois(sample_size, 7)
sample_poisson_8 <- stats::rpois(sample_size, 8)
sample_poisson_9 <- stats::rpois(sample_size, 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 = sample_poisson_1,
Poisson_2 = sample_poisson_2, Poisson_3 = sample_poisson_3, Poisson_4 = sample_poisson_4,
Poisson_5 = sample_poisson_5, Poisson_6 = sample_poisson_6, Poisson_7 = sample_poisson_7,
Poisson_8 = sample_poisson_8, Poisson_9 = sample_poisson_9)
## Obtain the heatmap for all sublists in the data.
HeatMap(data_samples)
## Obtain the heatmap for six random sublists in the data.
HeatMap(data_samples, selection = c(sample(1:length(data_samples), 6)))
## Obtain the heatmap for the binomial sublists in the data.
HeatMap(data_samples, selection = 1:9)
## Obtain the heatmap for the first 4 poisson sublists in the data.
HeatMap(data_samples, selection = 10:13)
## Obtain the heatmap for the last 5 poisson sublists in the data.
HeatMap(data_samples, selection = 14:18)