Function to Aggregate Directions From epplab Objects

Description

Function that automatically aggregates the projection directions from one or more epplab objects. Three options are available on how to choose the final projection which can have a rank larger than one. The parameter x can either be a single object or a list of epplab objects. Options for method are inverse, sq.inverse and cumulative.

Usage

EPPlabAgg(x, method = "cumulative", percentage = 0.85)

Arguments

Details

Denote p_i, i=1,...,m, the projection vectors contained in the list of epplab objects and P_i, i=1,..,m, the corresponding orthogonal projection matrices (each having rank one). The method cumulative is based on the eigenvalue decomposition of P_w=1/m sum(P_i) and transforms as O the eigenvectors such that the corresponding relative eigenvalues sum is at least percentage. The number of eigenvectors retained corresponds to the rank k and P is the corresponding orthogonal projection matrix. The methods inverse and sq.inverse are automatic rules to choose the number of eigenvectors to retain as implemented by the function AOP.

Value

A list with class 'epplabagg' containing the following components:

Author(s)

Daniel Fischer, Klaus Nordhausen, Anne Ruiz-Gazen

References

Liski, E., Nordhausen, K., Oja, H. and Ruiz-Gazen, A. (201?), Combining Linear Dimension Reduction Estimates, to appear in the Proceedings of ICORS 2015, pp. ??-??.

See Also

EPPlab, AOP

Examples

 library(tourr)
 data(olive)
 # To keep the runtime short, maxiter and n.simu were chosen very 
 # small for demonstration purposes, real life applications would
 # rather choose larger values, e.g. n.simu=100, maxiter=200
 olivePP.kurt.max <-
   EPPlab(olive[,3:10],PPalg="PSO",PPindex="KurtosisMax",n.simu=10, maxiter=20)
 
 olivePP.fried <-
   EPPlab(olive[,3:10],PPalg="PSO",PPindex="Friedman",n.simu=10, maxiter=20)
 
 olivePPs <- list(olivePP.kurt.max, olivePP.fried)
 
 EPPlabAgg(olivePP.kurt.max)$k
 EPPlabAgg(olivePPs, "cum", 0.99)$k
 
 pairs(olivePP.kurt.max$x %*% EPPlabAgg(olivePPs, "cum", 0.99)$O,
       col=olive[,2], pch=olive[,1])
 
 
 olivAOP.sq <- EPPlabAgg(olivePPs, "inv")
 oliveProj <- olivePP.kurt.max$x %*% olivAOP.sq$O
 plot(density(oliveProj))
 rug(oliveProj[olive$region==1],col=1)
 rug(oliveProj[olive$region==2],col=2)
 rug(oliveProj[olive$region==3],col=3)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(REPPlab)
Loading required package: rJava
Loading required package: lattice
Loading required package: LDRTools
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/REPPlab/EPPlabAgg.Rd_%03d_medium.png", width=480, height=480)
> ### Name: EPPlabAgg
> ### Title: Function to Aggregate Directions From epplab Objects
> ### Aliases: EPPlabAgg
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
>  library(tourr)
>  data(olive)
>  # To keep the runtime short, maxiter and n.simu were chosen very 
>  # small for demonstration purposes, real life applications would
>  # rather choose larger values, e.g. n.simu=100, maxiter=200
>  olivePP.kurt.max <-
+    EPPlab(olive[,3:10],PPalg="PSO",PPindex="KurtosisMax",n.simu=10, maxiter=20)

Simulation 0... finished (I 15803.749809761 in 0.610s)
Simulation 1... finished (I 16328.441668756 in 0.192s)
Simulation 2... finished (I 12773.246010155 in 0.046s)
Simulation 3... finished (I 14864.979690514 in 0.024s)
Simulation 4... finished (I 16355.465310614 in 0.465s)
Simulation 5... finished (I 13945.732375627 in 0.266s)
Simulation 6... finished (I 15344.965578953 in 0.023s)
Simulation 7... finished (I 13813.099231429 in 0.026s)
Simulation 8... finished (I 16404.088045583 in 0.056s)
Simulation 9... finished (I 14205.050358039 in 0.025s)
Warning message:
In EpplabOutputConv(jepplab, maxiter) :
  There were 10 non-converged simulation runs!
>  
>  olivePP.fried <-
+    EPPlab(olive[,3:10],PPalg="PSO",PPindex="Friedman",n.simu=10, maxiter=20)
Simulation 0... finished (I 1.019258283 in 0.465s)
Simulation 1... finished (I 1.150387469 in 0.252s)
Simulation 2... finished (I 1.040278522 in 0.288s)
Simulation 3... finished (I 1.065690413 in 0.222s)
Simulation 4... finished (I 1.121890739 in 0.250s)
Simulation 5... finished (I 1.177554547 in 0.294s)
Simulation 6... finished (I 1.166704982 in 0.223s)
Simulation 7... finished (I 1.161123983 in 0.221s)
Simulation 8... finished (I 1.171488662 in 0.221s)
Simulation 9... finished (I 1.131404919 in 0.222s)
Warning message:
In EpplabOutputConv(jepplab, maxiter) :
  There were 10 non-converged simulation runs!
>  
>  olivePPs <- list(olivePP.kurt.max, olivePP.fried)
>  
>  EPPlabAgg(olivePP.kurt.max)$k
[1] 1
>  EPPlabAgg(olivePPs, "cum", 0.99)$k
[1] 4
>  
>  pairs(olivePP.kurt.max$x %*% EPPlabAgg(olivePPs, "cum", 0.99)$O,
+        col=olive[,2], pch=olive[,1])
>  
>  
>  olivAOP.sq <- EPPlabAgg(olivePPs, "inv")
>  oliveProj <- olivePP.kurt.max$x %*% olivAOP.sq$O
>  plot(density(oliveProj))
>  rug(oliveProj[olive$region==1],col=1)
>  rug(oliveProj[olive$region==2],col=2)
>  rug(oliveProj[olive$region==3],col=3)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>