Dear @moneta,

I am using the TRExFitter for the fits so I am not sure how to print the full log of the fit.

I have tried to run the minimization using the RooWorkspace created by the TRExFitter, and here is the printed output:

```
[#1] INFO:Minimization -- p.d.f. provides expected number of events, including extended term in likelihood.
RooAbsTestStatistic::initSimMode: creating slave calculator #0 for state CR1_BDTG_WjZj (10 dataset entries)
RooAbsTestStatistic::initSimMode: creating slave calculator #1 for state CR2_BDTG_WjVV (10 dataset entries)
RooAbsTestStatistic::initSimMode: creating slave calculator #2 for state CR3_BDTG_WjST (10 dataset entries)
RooAbsTestStatistic::initSimMode: creating slave calculator #3 for state CR4_BDTG_tt (8 dataset entries)
RooAbsTestStatistic::initSimMode: creating slave calculator #4 for state SR_BDTG_tt (8 dataset entries)
[#1] INFO:Fitting -- RooAbsTestStatistic::initSimMode: created 5 slave calculators.
[#1] INFO:Minimization -- Including the following constraint terms in minimization: (lumiConstraint,alpha_DummyConstraint)
[#1] INFO:Minimization -- The following global observables have been defined and their values are taken from the model: (nominalLumi,nom_alpha_Dummy)
[#1] INFO:Minimization -- The following expressions have been identified as constant and will be precalculated and cached: (ttbar_CR1_BDTG_WjZj_shapes,Wjets_CR1_BDTG_WjZj_shapes,Zjets_CR1_BDTG_WjZj_shapes,Top_CR1_BDTG_WjZj_shapes,tW_CR1_BDTG_WjZj_shapes,VV_CR1_BDTG_WjZj_shapes)
[#1] INFO:Minimization -- The following expressions have been identified as constant and will be precalculated and cached: (ttbar_CR2_BDTG_WjVV_shapes,Wjets_CR2_BDTG_WjVV_shapes,Zjets_CR2_BDTG_WjVV_shapes,Top_CR2_BDTG_WjVV_shapes,tW_CR2_BDTG_WjVV_shapes,VV_CR2_BDTG_WjVV_shapes)
[#1] INFO:Minimization -- The following expressions have been identified as constant and will be precalculated and cached: (ttbar_CR3_BDTG_WjST_shapes,Wjets_CR3_BDTG_WjST_shapes,Zjets_CR3_BDTG_WjST_shapes,Top_CR3_BDTG_WjST_shapes,tW_CR3_BDTG_WjST_shapes,VV_CR3_BDTG_WjST_shapes)
[#1] INFO:Minimization -- The following expressions have been identified as constant and will be precalculated and cached: (ttbar_CR4_BDTG_tt_shapes,Wjets_CR4_BDTG_tt_shapes,Zjets_CR4_BDTG_tt_shapes,Top_CR4_BDTG_tt_shapes,tW_CR4_BDTG_tt_shapes,VV_CR4_BDTG_tt_shapes)
[#1] INFO:Minimization -- The following expressions have been identified as constant and will be precalculated and cached: (ttbar_SR_BDTG_tt_shapes,Wjets_SR_BDTG_tt_shapes,Zjets_SR_BDTG_tt_shapes,Top_SR_BDTG_tt_shapes,tW_SR_BDTG_tt_shapes,VV_SR_BDTG_tt_shapes)
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_simPdf_obsData_with_constr) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
Minuit2Minimizer: Minimize with max-calls 3500 convergence for edm < 1 strategy 1
Info in <Minuit2>: MnSeedGenerator Computing seed using NumericalGradient calculator
[#1] INFO:Minimization -- RooNLLVar::evaluatePartition(CR1_BDTG_WjZj) first = 0 last = 10 Likelihood offset now set to 7766.13
[#1] INFO:Minimization -- RooNLLVar::evaluatePartition(CR2_BDTG_WjVV) first = 0 last = 10 Likelihood offset now set to 1422.32
[#1] INFO:Minimization -- RooNLLVar::evaluatePartition(CR3_BDTG_WjST) first = 0 last = 10 Likelihood offset now set to 1469.18
[#1] INFO:Minimization -- RooNLLVar::evaluatePartition(CR4_BDTG_tt) first = 0 last = 8 Likelihood offset now set to 1014.87
[#1] INFO:Minimization -- RooNLLVar::evaluatePartition(SR_BDTG_tt) first = 0 last = 8 Likelihood offset now set to 1003.17
Info in <Minuit2>: MnSeedGenerator Initial state: FCN = -0.4647080266 Edm = -1290.472608 NCalls = 29
Info in <Minuit2>: NegativeG2LineSearch Doing a NegativeG2LineSearch since one of the G2 component is negative
Info in <Minuit2>: MnSeedGenerator Negative G2 found - new state:
Minimum value : -207.0313734
Edm : 555.0768522
Internal parameters: [ -1.370461484 -0.8368124947 -1.013527059 -0.9392015524 -1.370461484 -1.370461484 0]
Internal gradient : [ -385.3448371 -126.9227712 -73.89901187 -130.1838595 -7356.334857 -837.4527755 0]
Internal covariance matrix:
[[ 0.00030535353 0 0 0 0 0 0]
[ 0 0.011531951 0 0 0 0 0]
[ 0 0 0.021671137 0 0 0 0]
[ 0 0 0 0.038365674 0 0 0]
[ 0 0 0 0 4.6955167e-06 0 0]
[ 0 0 0 0 0 0.0013781427 0]
[ 0 0 0 0 0 0 0.080000106]]]
Info in <Minuit2>: MnSeedGenerator Initial state
Minimum value : -207.0313734
Edm : 555.0768522
Internal parameters: [ -1.370461484 -0.8368124947 -1.013527059 -0.9392015524 -1.370461484 -1.370461484 0]
Internal gradient : [ -385.3448371 -126.9227712 -73.89901187 -130.1838595 -7356.334857 -837.4527755 0]
Internal covariance matrix:
[[ 0.00030535353 0 0 0 0 0 0]
[ 0 0.011531951 0 0 0 0 0]
[ 0 0 0.021671137 0 0 0 0]
[ 0 0 0 0.038365674 0 0 0]
[ 0 0 0 0 4.6955167e-06 0 0]
[ 0 0 0 0 0 0.0013781427 0]
[ 0 0 0 0 0 0 0.080000106]]]
Info in <Minuit2>: VariableMetricBuilder Start iterating until Edm is < 0.001 with call limit = 3500
Info in <Minuit2>: VariableMetricBuilder 0 - FCN = -207.0313734 Edm = 555.0768522 NCalls = 165
Info in <Minuit2>: VariableMetricBuilder 1 - FCN = -271.818145 Edm = 35.44425486 NCalls = 187
Info in <Minuit2>: VariableMetricBuilder 2 - FCN = -285.1427466 Edm = 1.645456416 NCalls = 203
Info in <Minuit2>: VariableMetricBuilder 3 - FCN = -288.5749699 Edm = 0.9908530248 NCalls = 223
Info in <Minuit2>: VariableMetricBuilder 4 - FCN = -289.8092066 Edm = 0.1492127473 NCalls = 240
Info in <Minuit2>: VariableMetricBuilder 5 - FCN = -290.0558553 Edm = 0.09165506029 NCalls = 256
Info in <Minuit2>: VariableMetricBuilder 6 - FCN = -290.2124467 Edm = 0.04249761954 NCalls = 272
Info in <Minuit2>: VariableMetricBuilder 7 - FCN = -290.2762099 Edm = 0.02926391403 NCalls = 289
Info in <Minuit2>: VariableMetricBuilder 8 - FCN = -290.3999072 Edm = 0.01734148502 NCalls = 307
Info in <Minuit2>: VariableMetricBuilder 9 - FCN = -290.419307 Edm = 0.002677926088 NCalls = 323
Info in <Minuit2>: VariableMetricBuilder 10 - FCN = -290.4211681 Edm = 0.000779832486 NCalls = 339
Info in <Minuit2>: VariableMetricBuilder 11 - FCN = -290.4225457 Edm = 7.215752011e-05 NCalls = 355
Info in <Minuit2>: VariableMetricBuilder After Hessian
Info in <Minuit2>: VariableMetricBuilder 12 - FCN = -290.4225457 Edm = 9.938653785e-05 NCalls = 405
Minuit2Minimizer : Valid minimum - status = 0
FVAL = -290.422545690361346
Edm = 9.93865378540374582e-05
Nfcn = 405
#mu = 1.45643 +/- 0.174111 (limited)
#mutop = 8.38406 +/- 3.12646 (limited)
#mutw = 5.58026e-06 +/- 2.406 (limited)
#muvv = 15.5462 +/- 4.10719 (limited)
#muw = 1.07678 +/- 0.0286011 (limited)
#muz = 2.36066 +/- 0.277938 (limited)
alpha_Dummy = 0 +/- 0.993348 (limited)
```

indeed, the fit value for the 3rd parameter is close to its lower limit.

Minos gives the upper error close to the one printed above:

```
Info in <Minuit2>: MnMinos end of Minos scan for up interval for parameter #mutw
Minos: Parameter : #mutw is at Lower limit; error is -5.58026e-06
Minos: Lower error for parameter #mutw : -5.58026e-06
Minos: Upper error for parameter #mutw : 2.24958
Warning in <Minuit2>: RunMinosError Lower error for parameter #mutw is at the Lower limit!
```

If I allow the 3rd parameter to be negative, then it becomes `#mutw -1.2886e+01 +/- 7.85e+00`

and the error matches to the sqrt from corresponding element in the covariance matrix.

Since the negative value of fit parameters are unphysical, I set the lower bounds at zero, and then I guess only the upper error from minos makes sense.

Best regards,

Archil