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Type: Package
Title: The Folding Test of Unimodality
Version: 1.0
Description: The basic algorithm to perform the folding test of unimodality. Given a dataset X (d dimensional, n samples), the test checks whether the distribution of the data are rather unimodal or rather multimodal. This package stems from the following research publication: Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018. <doi:10.1145/3219819.3219994>.
Encoding: UTF-8
License: GPL-3
LazyData: true
RoxygenNote: 6.1.0
Suggests: testthat,MASS,knitr,rmarkdown
Imports: stats
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-09-19 07:23:46 UTC; asr
Author: Alban Siffer [aut, cre], Amossys [cph, fnd]
Maintainer: Alban Siffer <alban.siffer@irisa.fr>
Repository: CRAN
Date/Publication: 2018-09-30 13:10:12 UTC

Computes the folding ratio of the input data

Description

Computes the folding ratio of the input data

Usage

folding.ratio(X)

Arguments

X

nxd matrix (n observations, d dimensions)

Value

the folding ratio

Examples

X = matrix(runif(n = 1000, min = 0., max = 1.), ncol = 1)
phi = folding.statistics(X)


Computes the folding statistics of the input data

Description

Computes the folding statistics of the input data

Usage

folding.statistics(X)

Arguments

X

nxd matrix (n observations, d dimensions)

Value

the folding statsistics

Examples

library(MASS)
mu = c(0,0)
Sigma = matrix(c(1,0.5,1,0.5), ncol = 2)
X = mvrnorm(n = 5000, mu = mu, Sigma = Sigma)
Phi = folding.statistics(X)


Perform the folding test of unimodality

Description

Perform the folding test of unimodality

Usage

folding.test(X)

Arguments

X

$nxd$ matrix (n observations, d dimensions)

Value

1 if unimodal, 0 if multimodal

Examples

library(MASS)
n = 10000
d = 3
mu = c(0,0,0)
Sigma = matrix(c(1,0.5,0.5,0.5,1,0.5,0.5,0.5,1), ncol = d)
X = mvrnorm(n = n, mu = mu, Sigma = Sigma)
m = folding.test(X)


Computes the confidence bound for the significance level p

Description

Computes the confidence bound for the significance level p

Usage

folding.test.bound(n, d, p)

Arguments

n

sample size

d

dimension

p

significance level (between 0 and 1, the lower, the more significant)

Value

the confidence bound q (the bounds are 1-q and 1+q)

Examples

n = 2000  	# number of observations
d = 2 	  	# 2 dimensional data
p = 0.05  	# we want the bound at the level 0.05 (classical p-value)
q = folding.test.bound(n,d,p)


Computes the p-value of the folding test

Description

Computes the p-value of the folding test

Usage

folding.test.pvalue(Phi, n, d)

Arguments

Phi

the folding statistics

n

sample size

d

dimension

Value

the p-value (the lower, the more significant)

Examples

library(MASS)
n = 5000
d = 2
mu = c(0,0)
Sigma = matrix(c(1,0.5,1,0.5), ncol = d)
X = mvrnorm(n = n, mu = mu, Sigma = Sigma)
Phi = folding.statistics(X)
p = folding.test.pvalue(Phi,n,d)


Computes the pivot s_2 (approximate pivot)

Description

Computes the pivot s_2 (approximate pivot)

Usage

pivot.approx(X)

Arguments

X

nxd matrix (n observations, d dimensions)

Value

the approximate pivot

Examples

library(MASS)
mu = c(0,0)
Sigma = matrix(c(1,0.5,1,0.5), ncol = 2)
X = mvrnorm(n = 5000, mu = mu, Sigma = Sigma)
Phi = pivot.approx(X)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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