closer_grid_archetypal {GeomArchetypal}

Performs the Archetypal Analysis of a Data Frame by using as Archetypes the Closer to The Archetypal Grid Data Points

Description

the closer points to the archetypal grid are used as archetypes and then every data point is being expressed as a convex combination of those by using a modified PCHA method.

Usage

closer_grid_archetypal(dg, 
                       diag_less = 1e-2,
                       niter=30, 
                       use_seed = NULL,
                       verbose = TRUE)                       

Arguments

dg

The data frame with dimensions n x d

diag_less

The expected mean distance from 1 for the diagonal elements of submatrix A[irows,:], where irows are the closer to the Grid Archetypal data rows.

niter

The number of iterations that the A-update step should be done.

use_seed

If it is not NULL, then is used at the set.seed() for reproducibility reasons

verbose

If it is set to TRUE, then both initialization and iteration details are printed out

Details

The archetypal grid is being computed by taking the expand grid of the [Ximin,Ximax], i=1,...,d of all available variables. Then distances of all data points from that grid are calculated and the closer set of vectors is chosen.

Value

An object of class closer_grid_archetypal which is a list with members:

1. grid, the archetypal grid

2. grid_rows, the rows of the data frame that formed the archetypal grid

3. aa, an object of class archetypal

See Also

grid_archetypal

Examples

    
# Load package
library(GeomArchetypal)  
# Create random data
set.seed(20140519)
df=matrix(runif(90) , nrow = 30, ncol=3) 
colnames(df)=c("x","y","z")
# Closer Grid Archetypal
cga=closer_grid_archetypal(df, 
             diag_less = 1e-2, 
             niter = 150, 
             verbose = FALSE)
# Print the class "closer_grid_archetypal":
print(cga)
# Summary of the class "closer_grid_archetypal":
summary(cga)
# Plot the class "closer_grid_archetypal":
plot(cga)
# Observe the Closer Grid Archetypes near the 8 corners of the cube ...