PP_Optimizer {Pursuit} | R Documentation |
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)
Arguments
data |
Numeric dataset without class information. |
class |
Vector with names of data classes. |
findex |
Projection index function to be used: |
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). |
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. |
Author(s)
Paulo Cesar Ossani
Marcelo Angelo Cirillo
References
COOK, D., LEE, E. K., BUJA, A., WICKHAM, 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
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