--- /dev/null
+LogN <-
+function(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength)
+{
+ #2 condition case (skip the loop then maybe run faster? Code multi condition cases later)
+
+ #For each gene (m rows of Input---m genes)
+ #Save each gene's F0, F1 for further likelihood calculation.
+
+ #Get F0 for EE
+ F0=f0(Input, AlphaIn, BetaIn, EmpiricalR, NumOfEachGroup, log=F)
+ #Get F1 for DE
+ F1=f1(InputSP[[1]], InputSP[[2]], AlphaIn, BetaIn, EmpiricalRSP[[1]],EmpiricalRSP[[2]], NumOfEachGroup, log=F)
+
+ #Get z
+ #Use data.list in logfunction
+
+ z.list=PIn*F1/(PIn*F1+(1-PIn)*F0)
+ zNaNName=names(z.list)[is.na(z.list)]
+ zGood=which(!is.na(z.list))
+ ###Update P
+ #PFromZ=sapply(1:NoneZeroLength,function(i) sum(z.list[[i]])/length(z.list[[i]]))
+ PFromZ=sum(z.list[zGood])/length(z.list[zGood])
+ F0Good=F0[zGood]
+ F1Good=F1[zGood]
+ ### MLE Part ####
+ # Since we dont wanna update p and Z in this step
+ # Each Ng for one row
+
+ NumGroupVector=rep(c(1:NoneZeroLength),NumOfEachGroup)
+
+ NumGroupVector.zGood=NumGroupVector[zGood]
+ NumOfEachGroup.zGood=tapply(NumGroupVector.zGood,NumGroupVector.zGood,length)
+
+ StartValue=c(AlphaIn, BetaIn,PIn)
+
+ Result<-optim(StartValue,Likefun,InputPool=list(InputSP[[1]][zGood,],InputSP[[2]][zGood,],Input[zGood,],z.list[zGood], NoneZeroLength,EmpiricalR[zGood, ],EmpiricalRSP[[1]][zGood,], EmpiricalRSP[[2]][zGood,], NumOfEachGroup.zGood))
+ #LikeOutput=Likelihood( StartValue, Input , InputSP , PNEW.list, z.list)
+ AlphaNew= Result$par[1]
+ BetaNew=Result$par[2:(1+NoneZeroLength)]
+ PNew=Result$par[2+NoneZeroLength]
+ ##
+ Output=list(AlphaNew=AlphaNew,BetaNew=BetaNew,PNew=PNew,ZNew.list=z.list,PFromZ=PFromZ, zGood=zGood, zNaNName=zNaNName,F0Out=F0Good, F1Out=F1Good)
+ Output
+ }
+