ABCanalysisPlot

vector[1:n] describes an array of data: n cases in rows of one variable

Data

integer, optional, for plot default:  LineType=0 for solid line; for other line codes see documentation about pch

LineType

integer, optional, width of Line, see <code>lwd</code> in <a href="/link/par?package=ABCanalysis&version=1.0.2" rd-options="" data-mini-rdoc="ABCanalysis::par">par</a>

LineWidth

boolean, optional, the ABC curve of the uniform distribution is shown in plot if TRUE (default)

ShowUniform

string, optional, see parameter <code>main</code> in <a href="/link/plot?package=ABCanalysis&version=1.0.2" rd-options="" data-mini-rdoc="ABCanalysis::plot">plot</a>

title

boolean, = TRUE, lines of division in A, B and C are drawn, default = FALSE

limits

boolean, optional, default= TRUE, Mark the three points of interest

MarkPoints

optional, see <a href="/link/ABCcurve?package=ABCanalysis&version=1.0.2" rd-options="" data-mini-rdoc="ABCanalysis::ABCcurve">ABCcurve</a>

ABCcurvedata

Displays ABC Curve : cumulative percentage of largest Data (effort) vs cumlative percentage of sum of largest data (yield)
 with set limits generated by an calculated ABCanalysis.

ABCanalysis

ABC analysis

For a given data set, the package provides a novel method of computing precise limits to acquire subsets which are easily interpreted. Closely related to the Lorenz curve, the ABC curve visualizes the data by graphically representing the cumulative distribution function. Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on Ultsch. A ., Lotsch J.: Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data, PLoS One, 2015.

ABCanalysisPlot: Displays ABC plot with ABCanalysis

Description

Usage

Arguments

Value

See Also

Examples