RooArgSet* | fAltPOI | |
RooStats::ProfileLikelihoodTestStat | fAltProfile | |
RooArgSet* | fDetailedOutput | |
bool | fDetailedOutputEnabled | |
RooStats::ProfileLikelihoodTestStat | fNullProfile | |
Bool_t | fSubtractMLE | |
static Bool_t | fgAlwaysReuseNll |
returns -logL(poi, conditonal MLE of nuisance params) subtract off the global MLE or not depending on the option It is the numerator or the denominator of the ratio (depending on the pdf) L.M. : not sure why this method is needed now
evaluate the ratio of profile likelihood
Calculates the ratio of profiled likelihoods. By default the calculation is: Lambda(mu_alt , conditional MLE for alt nuisance) log -------------------------------------------- Lambda(mu_null , conditional MLE for null nuisance) where Lambda is the profile likeihood ratio, so the MLE for the null and alternate are subtracted off. If SetSubtractMLE(false) then it calculates: L(mu_alt , conditional MLE for alt nuisance) log -------------------------------------------- L(mu_null , conditional MLE for null nuisance) The values of the parameters of interest for the alternative hypothesis are taken at the time of the construction. If empty, it treats all free parameters as nuisance parameters. The value of the parameters of interest for the null hypotheses are given at each call of Evaluate(data,nullPOI).
set the conditional observables which will be used when creating the NLL so the pdf's will not be normalized on the conditional observables when computing the NLL
Returns detailed output. The value returned by this function is updated after each call to Evaluate(). The returned RooArgSet contains the following for the alternative and null hypotheses: <ul> <li> the minimum nll, fitstatus and convergence quality for each fit </li> <li> for each fit and for each non-constant parameter, the value, error and pull of the parameter are stored </li> </ul>
const bool PValueIsRightTail(void) { return false; } // overwrites default
{fSubtractMLE = subtract;}