(calibration_markov_chain_diagnostics_draw_ratio)=
# Drawing the acceptation ratio

This method is used to visualise the evolution of the acceptation ratio across iterations. It should be used to 
check the stability of the chains, since an unstable acceptation ratio indicates that the chains may not have 
converged. It also allows the user to verify that the acceptation ratio stabilizes within the desired range (between 
20% and 50%), which ensures good exploration of the target density while retaining a sufficient number of samples.

Thus, acceptation ratio plots provide useful guidance for determining an appropriate **burn-in** (the number of iterations 
to discard before stability is reached) and for selecting a suitable initial standard deviation of the proposal 
distribution to achieve an acceptation ratio within the desired range. The prototype is:

````{only} cpp
```cpp
void drawAcceptationRatio(TString sTitre, const char *variable = "*", Option_t * option = "");
```
````

````{only} py
```python
drawAcceptationRatio(sTitre, variable = "*", option = "")
```
````

This method takes up to three arguments, two of which are optional:

1. **sTitre**: the title of the plot (an empty string is allowed);

2. **variable** (optional): a list of parameter names to be drawn, separated by colons ":". The default `"*"` draws all parameters;

3. **option** (optional): a list of options, separated by commas ",", to adjust the plotting behavior:
                    
     - **"nonewcanvas"**: draw on the current canvas (instead of creating a new one);

     - **"vertical"**: if multiple parameters are plotted, display them stacked vertically (one per row). By default, 
     plots are arranged horizontally, side by side;

Returning to the example in [](#use_cases_macro_calibration_mcmc), the acceptation ratio plots (shown in 
{numref}`usecases_calibrationMCMCFlowrate1D_Acc`) reveal that the acceptation ratio is unstable during the first 
iterations, likely because the chains were initialised far from the most probable value. Afterward, the acceptation 
ratio stabilizes, oscillating around what appears to be an asymptote. Based on this, a burn-in of about 50 can be 
considered appropriate. When a acceptation ratio plot is drawn after defining a burn-in with `setBurnin`, the 
burn-in region is indicated by a black dotted line.