I am currently trying to optimize a model which may have multiple local minimum and variants.

I am using ordinal regression and maximum likelihood with fit = sm.optimizing(data=data, init=init, algorithm=‘BFGS’).

I am planning to run the model for a few times and try to output a maximum likelihood value that I can compare each time and choose the result that has the best maximum likelihood value. But I dont know how to output that value.

Can someone please kindly help me?

Thank you very much.

Regards,

PS

It’s in the fit object that’s return from `optimizing`

. Here’s the doc from R for `optimizing`

:

```
Value
par
The point estimate found. Its form (vector or list) is determined by the as_vector argument.
```

Hi Bob, thanks a lot for the answer. But in the fit object, I only find the values for the parameters. What I mean is the predicted value of Y each that that I will use to compare with the real Y. For example y ~ normal (). So I want to know each time, what value does the ~ normal gives me.

Thank you

I don’t understand what you want here. The parameter values are the maximum likelihood estimate.

If you want the predicted values of `y`

, it depends on your model and you’ll need to save the values as transformed parameters or generated quantities.

The `~`

only gives you an increment to the log density. It does *not* do sampling from the right-hand side. You can use the RNG functions in generated quantities to simulate if that’s what you want to do.