sym.interval.pc: Compute a symbolic interval principal components curves

In RSDA: R to Symbolic Data Analysis

Compute a symbolic interval principal components curves

Description

Compute a symbolic interval principal components curves

Usage

sym.interval.pc(sym.data, method = c('vertex', 'centers'), maxit, plot, scale, center)

Arguments

sym.data	Shoud be a symbolic data table read with the function read.sym.table(...)
method	It should be 'vertex' or 'centers'.
maxit	Maximum number of iterations.
plot	TRUE to plot immediately, FALSE if you do not want to plot.
scale	TRUE to standardize the data.
center	TRUE to center the data.

Value

prin.curve: This a symbolic data table with the interval principal components. As this is a symbolic data table we can apply over this table any other symbolic data analysis method (symbolic propagation).

cor.ps: This is the interval correlations between the original interval variables and the interval principal components, it can be use to plot the symbolic circle of correlations.

Author(s)

Jorge Arce.

References

Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.

Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.

Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.

Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.

Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.

See Also

sym.interval.pca

Examples

## Not run: 
data(oils)
res.vertex.ps <- sym.interval.pc(oils, "vertex", 150, FALSE, FALSE, TRUE)
class(res.vertex.ps$sym.prin.curve) <- c("sym.data.table")
sym.scatterplot(res.vertex.ps$sym.prin.curve[, 1], res.vertex.ps$sym.prin.curve[, 2],
  labels = TRUE, col = "red", main = "PSC Oils Data"
)

data(facedata)
res.vertex.ps <- sym.interval.pc(facedata, "vertex", 150, FALSE, FALSE, TRUE)
class(res.vertex.ps$sym.prin.curve) <- c("sym.data.table")
sym.scatterplot(res.vertex.ps$sym.prin.curve[, 1], res.vertex.ps$sym.prin.curve[, 2],
  labels = TRUE, col = "red", main = "PSC Face Data"
)

## End(Not run)

RSDA documentation built on Nov. 10, 2023, 5:06 p.m.