Nothing
gbess.glm = function(x, y, Gi, beta0, intercept=0, s, max.steps = 10, glm.max=1e6,
weights=rep(1,nrow(x)), normalize=FALSE)
{
if(length(unique(y))!=2) stop("Please input binary variable!")
if(missing(beta0)) beta0=rep(0,ncol(x))
if(s>length(beta0))
{stop("s is too large")}
# initial
n = dim(x)[1]
p = dim(x)[2]
vn = dimnames(x)[[2]]
one = rep(1,n)
names(beta0) = vn
xs = x
weights = weights/mean(weights)
orderGi = order(Gi)
x = x[,orderGi]
Gi = Gi[orderGi]
gi = unique(Gi)
gi_index = match(gi, Gi)
N = length(gi)
if(normalize)
{
meanx = drop(weights %*% x)/n
x = scale(x, meanx, FALSE)
normx = sqrt(drop(weights %*% (x^2)))
nosignal = normx/sqrt(n) < .Machine$double.eps
if (any(nosignal)) normx[nosignal] = (.Machine$double.eps) * sqrt(n)
names(normx) = NULL
x = sqrt(n)*scale(x, FALSE, normx)
}
beta = beta0
coef0 = intercept
A0 = NULL
B = rep(0,p+1)
for(k in 1:max.steps){
setA = gget_A(x, y, Gi, gi_index, s, beta, coef0, n, p, N, weights, B)
A = setA$A+1
B = setA$B+1
beta = rep(0,p)
gr_size = setA$gr_size
if(length(B)>=2)
{
logit=glmnet(x[,B],y,family="binomial",lambda = 0,maxit=glm.max, weights = weights)
beta[B]=logit$beta
coef0=logit$a0
}else{
logit=glm(y~x[,B],family="binomial", weights = weights)
beta[B]=logit$coefficients[-1]
coef0=logit$coefficients[1]
}
if(setequal(A,A0)==TRUE){
break;
}
A0 <- A
}
if(normalize)
{
beta=sqrt(n)*beta/normx
coef0=coef0-sum(beta*meanx)
}
beta[orderGi] = beta
names(beta) = vn
A = orderGi[A]
B = orderGi[B]
s=length(B)
eta = x%*%beta
pr = exp(eta)/(1+exp(eta))
xbest=xs[,which(beta!=0)]
bestmodel=glm(y~xbest, family="binomial", weights = weights)
dev=-2*sum((weights*((y*log(pr) + (1-y)*log(1-pr))))[which(pr>1e-20&pr<1-1e-20)])
nulldev=-2*sum(weights*(y*log(0.5) + (1-y)*log(0.5)))
aic=dev+2*s
bic=dev+log(n)*s
ebic=dev+(log(n)+2*log(p))*s
return(list(family="bess_binomial",beta=beta,coef0=coef0,nsample=n,bestmodel=bestmodel,
deviance=dev,nulldeviance=nulldev,
lambda=setA$max_T^2/2,p=p,AIC=aic,BIC=bic,EBIC=ebic,max.steps=max.steps,
gr_size=gr_size))
}
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