| Title: | Ad-Plot and Ud-Plot for Visualizing Distributional Properties and Normality |
|---|---|
| Description: | The empirical cumulative average deviation function introduced by the author is utilized to develop both Ad- and Ud-plots. The Ad-plot can identify symmetry, skewness, and outliers of the data distribution, including anomalies. The Ud-plot created by slightly modifying Ad-plot is exceptional in assessing normality, outperforming normal QQ-plot, normal PP-plot, and their derivations. The d-value that quantifies the degree of proximity between the Ud-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Full description of this methodology can be found in the article by Wijesuriya (2025) <doi:10.1080/03610926.2024.2440583>. |
| Authors: | Uditha Amarananda Wijesuriya [aut, cre] |
| Maintainer: | Uditha Amarananda Wijesuriya <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-03-01 07:56:51 UTC |
| Source: | CRAN |
Ad-plot identifies the characteristics of the distribution such as symmetry, skewness, and outliers of the data set.
adplot(X, title = "Ad-plot", xlab = "x", lcol = "black", rcol = "grey60", ...)adplot(X, title = "Ad-plot", xlab = "x", lcol = "black", rcol = "grey60", ...)
X |
an |
title |
title of the plot, Ad-plot by default. |
xlab |
|
lcol |
color of the points corresponding to the data that are less than or equal to the sample average, black by default. |
rcol |
color of the points corresponding to the data that are greater than the sample average, grey60 by default. |
... |
other graphical parameters. |
Ad-plot
Wijesuriya, U. A. (2025). Ad-plot and Ud-plot for Determining Distributional Characteristics and Normality. Communications in Statistics-Theory and Methods, doi:10.1080/03610926.2024.2440583.
set.seed(0) X1 <- matrix(rnorm(50, mean = 2, sd = 5)) adplot(X1) X2 <- matrix(rf(50, df1 = 10, df2 = 5)) adplot(X2) X3 <- matrix(rbeta(50, shape1 = 10, shape2 = 2)) adplot(X3, title="", lcol = "blue", rcol = "red")set.seed(0) X1 <- matrix(rnorm(50, mean = 2, sd = 5)) adplot(X1) X2 <- matrix(rf(50, df1 = 10, df2 = 5)) adplot(X2) X3 <- matrix(rbeta(50, shape1 = 10, shape2 = 2)) adplot(X3, title="", lcol = "blue", rcol = "red")
Ud-plot developed by a slight modification of Ad-plot can be utilized to assess normality.
udplot(X, npdf = FALSE, title = ifelse(npdf == FALSE, "Ud-plot", "Ud-plot & Normal Density Curve"), xlab = "x", lcol = "black", rcol = "grey60", pdfcol = "red", ...)udplot(X, npdf = FALSE, title = ifelse(npdf == FALSE, "Ud-plot", "Ud-plot & Normal Density Curve"), xlab = "x", lcol = "black", rcol = "grey60", pdfcol = "red", ...)
X |
an |
npdf |
display of the estimated normal density curve in the Ud-plot, FALSE by default. |
title |
title of the plot, Ud-plot by default and Ud-plot & Normal Density Curve otherwise. |
xlab |
|
lcol |
color of the points corresponding to the data that are less than or equal to the sample average, black by default. |
rcol |
color of the points corresponding to the data that are greater than the sample average, grey60 by default. |
pdfcol |
color of the estimated normal density curve, red by default. |
... |
other graphical parameters. |
Ad-plot
Wijesuriya, U. A. (2025). Ad-plot and Ud-plot for Determining Distributional Characteristics and Normality. Communications in Statistics-Theory and Methods, doi:10.1080/03610926.2024.2440583.
set.seed(0) X1 <- matrix(rnorm(50, mean = 2, sd = 5)) udplot(X1) X2 <- matrix(rnorm(50, mean = 2, sd = 5)) udplot(X2, npdf = TRUE) X3 <- matrix(rnorm(500, mean = 2, sd = 5)) udplot(X3, npdf = TRUE, title = "", lcol = "blue", rcol = "red", pdfcol = "black")set.seed(0) X1 <- matrix(rnorm(50, mean = 2, sd = 5)) udplot(X1) X2 <- matrix(rnorm(50, mean = 2, sd = 5)) udplot(X2, npdf = TRUE) X3 <- matrix(rnorm(500, mean = 2, sd = 5)) udplot(X3, npdf = TRUE, title = "", lcol = "blue", rcol = "red", pdfcol = "black")