#-----------------------------------------------------------------------------
#
#-----------------------------------------------------------------------------

SERE_fun <- function(observed, laneTotals, TH=1) {
  #calculate lambda and expected values
  total <- sum(laneTotals)
  fullObserved <- observed[rowSums(observed)>TH,];
  fullLambda <- rowSums(fullObserved)/total;
  fullLhat <- fullLambda > 0;
  fullExpected<- outer(fullLambda, laneTotals);

  #keep values
  fullKeep <- which(rowSums(fullExpected) > 0);
  
  #calculate degrees of freedom (nrow*(ncol -1) >> number of parameters - number calculated parameter (lamda is calculated >> thus minus 1)
  #calculate pearson and deviance for all values
  oeFull <- rowSums((fullObserved[fullKeep,] - fullExpected[fullKeep,])^2/ fullExpected[fullKeep,]) # test for over dispersion
  dfFull <- ncol(observed)*length(fullKeep) - sum(fullLhat!=0);
  devFull <- rowSums( fullObserved[fullKeep,] * log(ifelse(fullObserved[fullKeep,]==0,1,fullObserved[fullKeep,])/fullExpected[fullKeep,]))
  return(sqrt(sum(oeFull)/dfFull));
}

#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------