guidedProjections: Guided Projections Data Transformation
In tortnertuwien/lop: Local Projection Based Data Analysis

Description Usage Arguments Details Value Author(s) References Examples

Computes the orthogonal and score distances and OSD for guided projections.

1	guidedProjections(data, k=10, osd=c("OD", "SD", "OD*SD")

`data`	A data matrix of n observations and p variables.
`k=10`	The number of observations used to define the projections.
`OSD`	Used as similarity measure for the replacement of observations and for the comparison of observations. OSD can be set to "OD" for orthogonal distances, "SD" for score distances and "OD*SD" for the product of orthogonal and score distances.

Computes the orthogonal and score distances and OSD for a series of projections, providing a description of the datastructure. Each projection is corresponds to the projection onto a space spanned by k observations. The observations get sequencially replaced based on a measure of similarity. Guided projections are capeable of revealing group structure and outliers in a dataset. The transformation is especially helpful for the following situations: * inhomogenous groups of observations (e.g. different covariance structure) * groups located in different subspaces

A list-object of class "guidedprojections"" containing the following matrices:

`OD`	A matrix of orthogonal distances for each observation (columns) to each projection space (rows)
`SD`	A matrix of score distances for each observation (columns) in each projection space (rows)
`OSD`	A matrix measures of similarity for each observation (columns) and projection (rows)

Thomas Ortner (thomas.ortner@tuwien.ac.at)

Guided projections for analysing the structure of high-dimensional data https://arxiv.org/abs/1702.06790

library(lop)
#might take a minute
gp <- guidedProjections(glass$data)
plot(gp, col=glass$group)

#For details on the data set see
#Lemberge, P., De Raedt, I., Janssens, K. H., Wei, F., and
#Van Espen, P. J. (2000). Quantitative analysis of 16–17th
#century archaeological glass vessels using pls regression of
#epxma and μ-xrf data. Journal of Chemometrics, 14(5-6):751–763.