gR2: gR2

In heatherjzhou/gR2.2: Generalized R Square Measures for Mixtures of Bivariate Linear Dependences

Description

Add description of gR2 here

Usage

gR2(x, y, z = NULL, K = NULL, cand.Ks = 1:4, nstart = 30,
  mc.cores = detectCores() - 1, inference = FALSE, conf.level = 0.95,
  method = c("general", "binorm"))

Arguments

x	Add description here
y	Add description here
z	Add description here
K	Add description here
cand.Ks	Add description here
nstart	Add description here
mc.cores	Add description here
inference	Add description here
conf.level	Add description here
method	Add description here

Details

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Add details here

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Value

gR2 returns a list consisting of the following items:

estimate	Add description here
conf.level	Add description here
conf.int	Add description here
p.val	Add description here
K	Add description here
membership	Add description here

Author(s)

Heather J Zhou, [e-mail]

Jingyi Jessica Li, [e-mail]

References

Li, J.J., Tong, X., and Bickel, P.J. (2018). Generalized R2 Measures for a Mixture of Bivariate Linear Dependences. arXiv.

Examples

library(mvtnorm)
#Simulate data from a mixture of bivariate normal distributions
n=200 #sample size
K=2 #number of components (lines)
p_s=c(0.5,0.5) #proportions of components
mu_s=list(c(0,-2),c(0,2)) #mean vectors
Sigma_s=list(rbind(c(1,0.8),c(0.8,1)),rbind(c(1,0.8),c(0.8,1))) #covariance matrices
z=sample(1:K,size=n,prob=p_s,replace=TRUE) #line memberships
data=matrix(0,nrow=n,ncol=2)
for (i in 1:K){
  idx=which(z==i)
  data[idx,]=rmvnorm(n=length(idx),mean=mu_s[[i]],sigma=Sigma_s[[i]])
}

x<-data[,1]
y<-data[,2]
plot(x,y)

#Specified scenario
gR2(x,y,z) #No inference
gR2(x,y,z,inference=TRUE) #Inference

#Unspecified scenario
gR2(x,y,K=2,mc.cores=2) #K chosen, no inference
#gR2(x,y,K=2,inference=TRUE,mc.cores=2) #K chosen, inference
#gR2(x,y,mc.cores=2) #K not chosen, no inference
#gR2(x,y,inference=TRUE,mc.cores=2) #K chosen, inference

heatherjzhou/gR2.2 documentation built on Nov. 14, 2019, 12:14 a.m.