Help for package adaplots

Type:

Package

Title:

Ada-Plot and Uda-Plot for Assessing Distributional Attributes and Normality

Version:

0.1.0

Maintainer:

Uditha Amarananda Wijesuriya <u.wijesuriya@usi.edu>

Description:

The centralized empirical cumulative average deviation function is utilized to develop both Ada-plot and Uda-plot as alternatives to Ad-plot and Ud-plot introduced by the author. Analogous to Ad-plot, Ada-plot can identify symmetry, skewness, and outliers of the data distribution. The Uda-plot is as exceptional as Ud-plot in assessing normality. The d-value that quantifies the degree of proximity between the Uda-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Extreme values in the data can be eliminated using the 1.5IQR rule to create its robust version if user demands. Full description of the methodology can be found in the article by Wijesuriya (2025a) <doi:10.1080/03610926.2025.2558108>. Further, the development of Ad-plot and Ud-plot is contained in both article and the 'adplots' R package by Wijesuriya (2025b & 2025c) <doi:10.1080/03610926.2024.2440583> and <doi:10.32614/CRAN.package.adplots>.

License:

GPL-3

Encoding:

UTF-8

Imports:

ggplot2

Suggests:

knitr, rmarkdown, testthat (≥ 3.0.0)

Config/testthat/edition:

VignetteBuilder:

knitr

RoxygenNote:

7.3.2

NeedsCompilation:

Packaged:

2025-09-29 13:16:20 UTC; uwijesuriy

Author:

Uditha Amarananda Wijesuriya [aut, cre]

Repository:

CRAN

Date/Publication:

2025-10-06 08:00:14 UTC

Creates Ada-plot for the provided data.

Description

Ada-plot identifies the characteristics of the distribution such as symmetry, skewness, and outliers of the data set.

Usage

adaplot(X, title = "Ada-plot", xlab = "x", lcol = "black", rcol = "grey60", ...)

Arguments

X

an n by 1 matrix, equivalently, a column vector of length n, where n is the number of observations.

title

title of the plot, Ada-plot by default.

xlab

x-axis label, x by default.

lcol

color of the points which correspond to data on or below the sample average, black by default.

rcol

color of the points which correspond to data above the sample average, grey60 by default.

...

other graphical parameters.

Value

Ada-plot

References

Wijesuriya, U. A. (2025a). Ada-plot and Uda-plot as Alternatives for Ad-plot and Ud-plot. Communications in Statistics-Theory and Methods, doi:10.1080/03610926.2025.2558108.

Wijesuriya, U. A. (2025b). Ad-plot and Ud-plot for Determining Distributional Characteristics and Normality. Communications in Statistics-Theory and Methods, doi:10.1080/03610926.2024.2440583.

Wijesuriya, U. A. (2025c). adplots: Ad-plot and Ud-plot for Visualizing Distributional Properties and Normality. R Package Version 0.1.0, doi:10.32614/CRAN.package.adplots.

Examples

   set.seed(0)
   X1 <- matrix(rnorm(50, mean = 2, sd = 5))
   adaplot(X1)

   X2 <- matrix(rf(50, df1 = 10, df2 = 5))
   adaplot(X2)

   X3 <- matrix(rbeta(50, shape1 = 10, shape2 = 2))
   adaplot(X3, title = "", lcol = "blue", rcol = "red")

Creates Uda-plot for the provided data with and without the estimated normal density curve, excluding extreme values.

Description

Uda-plot can be utilized to assess normality.

Usage

udaplot(X, excld = FALSE, npdf = FALSE, title = ifelse(npdf == FALSE, "Uda-plot",
       "Uda-plot & Normal Density Curve"), xlab = "x", lcol = "black", rcol = "grey60",
       pdfcol = "red",...)

Arguments

X

an n by 1 matrix, equivalently, a column vector of length n, where n is the number of observations.

excld

exclude extreme values in the data applying 1.5IQR rule, FALSE by default.

npdf

display of the estimated normal density curve in the Uda-plot, FALSE by default.