# BinomialEMVS: Variable Selection For Binary Data Using The EM Algorithm In BinaryEMVS: Variable Selection for Binary Data Using the EM Algorithm

## Description

Conducts EMVS analysis

## Usage

 ```1 2 3 4 5``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```#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) ```

BinaryEMVS documentation built on May 30, 2017, 5:14 a.m.