gapStatistics: gap statistics

Description Usage Arguments Details Value Author(s) Examples

Description

gap statistics to tune gamma

Usage

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gapStatistics(d, K = 3, B = 10, gammas = NULL, alpha = 1,
  group = NULL, seed = 15213, silence = FALSE)

Arguments

d

A list of S studies, each study is a combined data matrix n*J, where n is number of subjects, J=J1+J2+... and J1 is number of features in omics dataset 1 and J2 is number of features in omics dataset 2...

K

number of clusters

B

number of permutations.

alpha

balance between group sparsity and individual sparsity. alpha=1 yeilds no group sparsity. alpha=0 yeilds no individual penalty.

group

Prior group information. Potentially these group can contain overlap features. group is a list and each element of the list is feature index.

seed

random seed

silence

do not print progress in silence = TRUE.

gamma

Penalty on total number of features. Default is given. Larger gamma will yeild small number of selected features.

Details

gap statistics to tune gamma, the tuning parameter to control number of features.

Value

a table. Each row represents a gamma.

gamma

input gammas

score

objective score: see obj0 in ISKmeans function

E.score

mean value of permutated score

se.score

standard error of permutated score

gapStat

gap statistics, score - E.score

numF

number of selected features

Author(s)

Caleb

Examples

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S <- 2
K <- 3
G <- 1000
g1 <- 50
g2 <- 50
n0 <- 20
n <- K*n0
labels <- cut(1:n,breaks=K,labels=FALSE)

set.seed(32611)
S1 <- matrix(rnorm(G*n), nrow=G, ncol=n)
S2 <- matrix(rnorm(G*n), nrow=G, ncol=n)

S1[1:g1, labels==1] <- S1[1:g1, labels==1] + 2
S1[1:g1, labels==3] <- S1[1:g1, labels==3] - 2
S1[g1 + 1:g2, labels==1] <- S1[g1 + 1:g2, labels==1] - 2
S1[g1 + 1:g2, labels==2] <- S1[g1 + 1:g2, labels==2] + 2

S2[1:g1, labels==2] <- S2[1:g1, labels==2] + 2
S2[1:g1, labels==1] <- S2[1:g1, labels==1] - 2
S2[g1 + 1:g2, labels==2] <- S2[g1 + 1:g2, labels==2] - 2
S2[g1 + 1:g2, labels==3] <- S2[g1 + 1:g2, labels==3] + 2

S = list(t(S1),t(S2))
groups <- Map('c',1:g1,g1 + 1:g2)

gapResult <- gapStatistics(d=S,K=3,B=10,group=groups)
print(gapResult)

Caleb-Huo/MIS-Kmeans documentation built on May 17, 2019, 2:45 p.m.