fast_archetypal {GeomArchetypal}

Performs the Archetypal Analysis of a Data Frame by using a Given Set of Archetypes

Description

Performs the archetypal analysis of a data frame by using a known set of archetypes as rows of the data matrix.

Usage

fast_archetypal(df,                 
                irows, 
                diag_less = 1e-2,
                niter = 30, 
                verbose = TRUE, 
                data_tables = TRUE,
                use_seed = NULL)

Arguments

df

The data frame with dimensions n x d

irows

The rows from data frame that represent the archetypes

diag_less

The expected mean distance from 1 for the diagonal elements of submatrix A[irows,:]

niter

The number of times that the A-update process should be done

verbose

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

data_tables

If it set to TRUE, then a data table for the initial data points will be computed

use_seed

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

Details

If we know the archetypes, then we can bypass the half part of PCHA and perform only the A-update part, that of computing the convex combinations for each data point. Then archetypal analysis is a fast procedure, since we need only to compute one matrix.

Value

An object of class 'archetypal' is returned.

See Also

grid_archetypal, closer_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-3, 
                           niter = 250, verbose = FALSE)
# The closer to the Grid Archetypes points - rows are:
crows = cga$grid_rows
print(crows)
# Now we call the fast_archetypal() with those rows as argument:
fa=fast_archetypal(df, irows = crows, diag_less = 1e-3, 
                   niter = 250, verbose = FALSE)
# Print:
print(fa)
# Summary:
summary(fa)
# Plot:
plot(fa)
# Results are identical to the closer_grid_archetypal() ones:
all.equal(cga$aa$BY,fa$BY)