MVar (version 2.2.7)

PP_Optimizer: Optimization function of the Projection Pursuit index (PP).

Description

Optimization function of the Projection Pursuit index (PP).

Usage

PP_Optimizer(data, class = NA, findex = "HOLES",   
             dimproj = 2, sphere = TRUE, optmethod = "GTSA",   
             weight = TRUE, lambda = 0.1, r = 1, cooling = 0.9,  
             eps = 1e-3, maxiter = 3000, half = 30)

Value

num.class

Number of classes.

class.names

Class names.

proj.data

Projected data.

vector.opt

Projection vectors found.

index

Vector with the projection indices found in the process, converging to the maximum, or the minimum.

findex

Projection index function used.

Arguments

data

Numeric dataset without class information.

class

Vector with names of data classes.

findex

Projection index function to be used:
"lda" - LDA index,
"pda" - PDA index,
"lr" - Lr index,
"holes" - Holes index (default),
"cm" - Central Mass index,
"pca" - PCA index,
"friedmantukey" - Friedman Tukey index,
"entropy" - Entropy index,
"legendre" - Legendre index,
"laguerrefourier" - Laguerre Fourier index,
"hermite" - Hermite index,
"naturalhermite" - Natural Hermite index,
"kurtosismax" - Maximum kurtosis index,
"kurtosismin" - Minimum kurtosis index,
"moment" - Moment index,
"mf" - MF index,
"chi" - Chi-square index.

dimproj

Dimension of the data projection (default = 2).

sphere

Spherical data (default = TRUE).

optmethod

Optimization method GTSA - Grand Tour Simulated Annealing or SA - Simulated Annealing (default = "GTSA").

weight

Used in index LDA, PDA and Lr to weight the calculations for the number of elements in each class (default = TRUE).

lambda

Used in the PDA index (default = 0.1).

r

Used in the Lr index (default = 1).

cooling

Cooling rate (default = 0.9).

eps

Approximation accuracy for cooling (default = 1e-3).

maxiter

Maximum number of iterations of the algorithm (default = 3000).

half

Number of steps without incrementing the index, then decreasing the cooling value (default = 30).

Author

Paulo Cesar Ossani

Marcelo Angelo Cirillo

References

Cook, D.; Lee, E. K.; Buja, A.; Wickmam, H. Grand tours, projection pursuit guided tours and manual controls. In Chen, Chunhouh, Hardle, Wolfgang, Unwin, e Antony (Eds.), Handbook of data Visualization, Springer Handbooks of Computational Statistics, chapter III.2, p. 295-314. Springer, 2008.

Lee, E.; Cook, D.; Klinke, S.; Lumley, T. Projection pursuit for exploratory supervised classification. Journal of Computational and Graphical Statistics, 14(4):831-846, 2005.

See Also

Plot.PP and PP_Index

Examples

data(iris) # data set

# Example 1 - Without the classes in the data
data <- iris[,1:4]

class <- NA # data class

findex <- "kurtosismax" # index function

dim <- 1 # dimension of data projection

sphere <- TRUE # spherical data

res <- PP_Optimizer(data = data, class = class, findex = findex,
                    optmethod = "GTSA", dimproj = dim, sphere = sphere, 
                    weight = TRUE, lambda = 0.1, r = 1, cooling = 0.9, 
                    eps = 1e-3, maxiter = 1000, half = 30)
 
print("Number of classes:"); res$num.class
print("class Names:"); res$class.names
print("Projection index function:"); res$findex
print("Projected data:"); res$proj.data
print("Projection vectors:"); res$vector.opt
print("Projection index:"); res$index


# Example 2 - With the classes in the data
class <- iris[,5] # classe dos dados

res <- PP_Optimizer(data = data, class = class, findex = findex,
                    optmethod = "GTSA", dimproj = dim, sphere = sphere, 
                    weight = TRUE, lambda = 0.1, r = 1, cooling = 0.9, 
                    eps = 1e-3, maxiter = 1000, half = 30)

print("Number of classes:"); res$num.class
print("class Names:"); res$class.names
print("Projection index function:"); res$findex
print("Projected data:"); res$proj.data
print("Projection vectors:"); res$vector.opt
print("Projection index:"); res$index