ErrorRate: This Function Calculates the True Error Rate of a Clustering...

In NCutYX: Clustering of Omics Data of Multiple Types with a Multilayer Network Representation

Description

This Function Calculates the True Error Rate of a Clustering Result, Assuming that There are Three Clusters.

Usage

ErrorRate(X, n1, n2)

Arguments

X	is a clustering result in matrix format.
n1	is the size of the first cluster.
n2	is the size of the second cluster.

Value

err is the true error rate of a clustering result.

References

Li, Yang; Bie, Ruofan; Teran Hidalgo, Sebastian; Qin, Yinchen; Wu, Mengyun; Ma, Shuangge. Assisted gene expression-based clustering with AWNCut. (Submitted.)

Examples

set.seed(123456)
#This sets up the initial parameters for the simulation.
lambda <- seq(2,6,1) #Tuning parameter lambda
Tau    <- seq(0.2,0.8,0.2) #Tuning parameter tau

n=30; n1=10; n2=10; n3=n-n1-n2 #Sample size
p1=10; p2=10; r1=8; r2=8; #Number of variables and noises in each dataset

K=3; #Number of clusters

mu=1; #Mean of the marginal distribution
u1=0.5; #Range of enties in the coefficient matrix

library(mvtnorm)
epsilon <- matrix(rnorm(n*(p1-r1),0,1), n, (p1-r1)) # Generation of random error

Sigma1 <- matrix(rep(0.8,(p1-r1)^2),(p1-r1),(p1-r1)) # Generation of the covariance matrix
diag(Sigma1) <- 1

# Generation of the original distribution of the three clusters
T1 <- matrix(rmvnorm(n1,mean=rep(-mu,(p1-r1)),sigma=Sigma1),n1,(p1-r1))
T2 <- matrix(rmvnorm(n2,mean=rep(0,(p1-r1)),sigma=Sigma1),n2,(p1-r1))
T3 <- matrix(rmvnorm(n3,mean=rep(mu,(p1-r1)),sigma=Sigma1),n3,(p1-r1))

X1 <- sign(T1)*(exp(abs(T1))) #Generation of signals in X
X2 <- sign(T2)*(exp(abs(T2)))
X3 <- sign(T3)*(exp(abs(T3)))
ep1 <- (matrix(rnorm(n*r1,0,1),n,r1)) #Generation of noises in X
X <- rbind(X1,X2,X3)

beta1 <- matrix(runif((p1-r1)*(p2-r2),-u1,u1),(p1-r1),(p2-r2)) #Generation of the coefficient matrix
Z     <- X%*%beta1+epsilon #Generation of signals in Z
ep2   <- (matrix(rnorm(n*r2,0.5,1),n,r2)) #Generation of noises in Z

X <- cbind(X,ep1)
Z <- cbind(Z,ep2)
#our method
Tune1         <- awncut.selection(X, Z, K, lambda, Tau, B = 20, L = 1000)
awncut.result <- awncut(X, Z, 3, Tune1$lam, Tune1$tau, B = 20, L = 1000)
ErrorRate(awncut.result[[1]]$Cs, n1, n2)

NCutYX documentation built on May 2, 2019, 3:15 a.m.