awncut.selection: This Function Outputs the Selection of Tuning Parameters for...

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

Description

This Function Outputs the Selection of Tuning Parameters for the AWNCut Method.

Usage

awncut.selection(X, Z, K, lambda, Tau, B = 500, L = 1000)

Arguments

X	is an n x p1 matrix of n observations and p1 variables.
Z	is an n x p2 matrix of n observations and p2 variables. Z is the assistant dataset.
K	is the number of clusters.
lambda	is a vector of tuning parameter lambda in the objective function.
Tau	is a vector of tuning parameter tau in the objective function.
B	is the number of iterations in the simulated annealing algorithm.
L	is the temperature coefficient in the simulated annealing algorithm. #' @return A list with the following components: num is the position of the max DBI Table is the Table of the DBI for all possible combination of the parameters lam is the best choice of tuning parameter lambda tau is the best choice of tuning parameter lambda DBI is the max DBI

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.