pp: Creates the projection pursuit function.

Description Usage Arguments Details Value Author(s) Examples

Description

These functions encapsulate everything, that is, the data, the benchmark and the index parameters, needed to compute the projection index.

Usage

1
pp(r = 0.8, n, data, oth, k)

Arguments

r

The radius multiplier. Values between 0.5 and 3 seem to work well.

n

Number of Monte-Carlo Evaluations to approximate the integral. Values as low as 25 can be used.

data

The data for which structure needs to be found.

oth

The benchmark dataset.

k

The target dimension.

Details

pp is for projection pursuit.

Value

The actual index function, which takes a single matrix argument, and returns the index value for that projection.

Author(s)

Mohit Dayal

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
##Exploring structure in the RANDU data
##Or using the MINSTD generator
randu <- as.matrix(randu)

randtoolbox::setSeed(570)
w <- randtoolbox::congruRand(1200)
dim(w) <- c(3, 400)
w <- t(w)

m <- 'geodesic'
a <- 0.50

ranif1 <- pp(r=1, n=50, data=randu, oth=w, k=2)

set.seed(50)
F1 <- basis_random(3)
o1 <- optim(par=F1, fn=ranif1, gr=basis_nearby(), method='SANN',
            control=list(fnscale=-1, maxit=100, trace=1))
plot(randu %*% o1$par)

##How accurate are the values?
ranif1hi <- pp(r=1, n=500, data=randu, oth=w, k=2)
ranif1hi(o1$par)


Search within the cepp package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.