+++ /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
- }
-