| logMINDEX {IDmining} | R Documentation |
The Multipoint Morisita Index in 1, 2 or Higher Dimensions
Description
Computes the ln values of the multipoint Morisita index in 1, 2 or higher dimensional spaces.
Usage
logMINDEX(X, scaleQ=1:5, mMin=2, mMax=2)
Arguments
X |
A |
scaleQ |
Either a single value or a vector. It contains the value(s) of |
mMin |
The minimum value of |
mMax |
The maximum value of |
Details
-
\ellis the edge length of the grid cells (or quadrats). Since the variables (and consenquently the grid) are rescaled to the[0,1]interval,\ellis equal to1for a grid consisting of only one cell. -
\ell^{-1}is the number of grid cells (or quadrats) along each axis of the Euclidean space in which the data points are embedded. -
\ell^{-1}is equal toQ^{(1/E)}whereQis the number of grid cells andEis the number of variables (or features). -
\ell^{-1}is directly related to\delta(see References). -
\deltais the diagonal length of the grid cells.
Value
A data.frame containing the \ln value of the m-Morisita index for each value of
\ln (\delta) and m. Notice also that the values of
\ln (\delta) are provided with regard to the [0,1] interval.
Author(s)
Jean Golay jeangolay@gmail.com
References
J. Golay and M. Kanevski (2015). A new estimator of intrinsic dimension based on the multipoint Morisita index, Pattern Recognition 48 (12):4070–4081.
Examples
sim_dat <- SwissRoll(1000)
m <- 2
scaleQ <- 1:15 # It starts with a grid of 1^E cell (or quadrat).
# It ends with a grid of 15^E cells (or quadrats).
lnmMI <- logMINDEX(sim_dat, scaleQ, m, m)
dev.new(width=5, height=4)
plot(exp(lnmMI[,1]),exp(lnmMI[,2]),pch=19,col="black",xlab="",ylab="")
title(xlab = expression(delta), cex.lab = 1.5,line = 2.5)
title(ylab = expression(I['2,'*delta]), cex.lab = 1.5,line = 2.5)
dev.new(width=5, height=4)
plot(lnmMI[,1],lnmMI[,2],pch=19,col="black",xlab="",ylab="")
title(xlab = expression(paste("log(",delta,")")), cex.lab = 1.5,line = 2.5)
title(ylab = expression(paste("log(",I['2,'*delta],")")), cex.lab = 1.5,line = 2.5)