BinomialEMVS {BinaryEMVS}

Variable Selection For Binary Data Using The EM Algorithm

Description

Conducts EMVS analysis

Usage

BinomialEMVS(y, x, type = "probit", epsilon = 5e-04, v0s = ifelse(type ==
  "probit", 0.025, 5), nu.1 = ifelse(type == "probit", 100, 1000),
  nu.gam = 1, lambda.var = 0.001, a = 1, b = ncol(x),
  beta.initial = NULL, sigma.initial = 1, theta.inital = 0.5, temp = 1,
  p = ncol(x), n = nrow(x), SDCD.length = 50)

Arguments

y

responses in 0-1 coding

x

X matrix

type

probit or logit model

epsilon

tuning parameter

v0s

tuning parameter, can be vector

nu.1

tuning parameter

nu.gam

tuning parameter

lambda.var

tuning parameter

a

tuning parameter

b

tuning parameter

beta.initial

starting values

sigma.initial

starting value

theta.inital

startng value

temp

not sure

p

not sure

n

not sure

SDCD.length

not sure

Value

probs is posterior probabilities

Examples

#Generate data
set.seed(1)
n=25;p=500;pr=10;cor=.6
X=data.sim(n,p,pr,cor)

#Randomly generate related beta coefficnets from U(-1,1)
beta.Vec=rep(0,times=p)
beta.Vec[1:pr]=runif(pr,-1,1)

y=scale(X%*%beta.Vec+rnorm(n,0,sd=sqrt(3)),center=TRUE,scale=FALSE)
prob=1/(1+exp(-y))
y.bin=t(t(ifelse(rbinom(n,1,prob)>0,1,0)))

result.probit=BinomialEMVS(y=y.bin,x=X,type="probit")
result.logit=BinomialEMVS(y=y.bin,x=X,type="logit")

which(result.probit$posts>.5)
which(result.logit$posts>.5)