Yamm-package | R Documentation |
This package provides functions for computing the projection median. PmedTrapz
approximates the projection median by the trapezoidal rule, which is only valid for the two- and three-dimensional cases, while PmedMCInt
use Monte Carlo approximation, and it is valid for any multivariate median. yamm
provides another method to compute the projection median based on an optimiser technique. This package also provides functions for plotting different multivariate medians, such as the Spatial, Component-wise, Tukey's, etc., for randomly generated data sets in both the two-dimensional and three-dimensional cases. In addition, this package also allows users to produce the two-dimensional and three-dimensional quantile plots with function muqie
and muqie3D
respectively.
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
Fan Chen [aut], Guy Nason [aut, cre]
Maintainer: Guy Nason <g.nason@imperial.ac.uk>
Basu, R., Bhattacharya, B.B., and Talukdar, T. (2012) The projection median of a set of points in Rd CCCG., 47, 329-346. doi: 10.1007/s00454-011-9380-6
Chen, F. and Nason, Guy P. (2020) A new method for computing the projection medi an, its influence curve and techniques for the production of projected quantile plots. PLOS One, doi: 10.1371/journal.pone.0229845
Croux, C., Filzmoser, P., and Oliveira, M., (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis, Chemometrics and Intelligent Laboratory Systems, 87, 218-225.
Durocher, S. and Kirkpatrick, D. (2009), The projection median of a set of points, Computational Geometry, 42, 364-375.
Rousseeuw, P.J. and Ruts, I. (1996), Algorithm AS 307: Bivariate location depth, Appl. Stat.-J. Roy. St. C, 45, 516-526.
Rousseeuw, P.J. and Ruts, I. (1998), Constructing the bivariate Tukey median, Stat. Sinica, 8, 828-839.
Rousseeuw, P.J., Ruts, I., and Tukey, J.W. (1999), The Bagplot: A Bivariate Boxplot, The Am. Stat., 53, 382-387.
Struyf, A. and Rousseeuw, P.J. (2000), High-dimensional computation of the deepest location, Comput. Statist. Data Anal., 34, 415-436.
yamm
,
PmedTrapz
,
PmedMCInt
,
# Load a 2-dimensional data set. data(clusters2d) # # Set seed for reproduction. set.seed(5) # # Projection median approximated by Monte Carlo Integration. PmedMCInt(clusters2d, nprojs = 30000) # [1] 4.3369501 -0.1578591 # # # Projection median approximated by the trapezoidal rule. PmedTrapz(clusters2d,no.subinterval=180) # [1] 4.1556553 -0.3566614 # # # Yamm. set.seed(5) yamm(clusters2d,nprojs = 2500,reltol=1e-3,doabs=1,full.results=FALSE) # [1] 4.3871582 -0.1070497 # # # Plot 2-D medians # Remove the outliers of the dataset. cluster_without_outlier <- clusters2d[c(1:101),] myxvec <- c(min(cluster_without_outlier[,1]), max(cluster_without_outlier[,1])) myyvec <- c(min(cluster_without_outlier[,2]), max(cluster_without_outlier[,2])) # # Plot the figure. set.seed(5) Plot2dMedian(clusters2d, myxvec, myyvec, yamm.nprojs = 2000, PmedMCInt.nprojs = 20000, no.subinterval = 36, opt.method = "BFGS", xlab = "Component1", ylab = "Component2")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.