What's the meaning of sfit when estimating an ordered probit model?

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I'm estimating an ordered probit model, and I'd like to have some parameter indicating the goodness of fit (unlike other regression models, there are many proposed indicators to evaluate this). So I'd like to know:
1) What's the meaning of stats.sfit? The matlab help states "the estimated dispersion parameter", but I don't know what do they mean with that.
2) If sfit is not indicating goodness of fit, is there some way to obtain it directly from Matlab?

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Nikhil Reddy Pottanigari
Nikhil Reddy Pottanigari il 18 Giu 2020
Hi,
I cannot find stats.sfit function. So, I am giving general sfit function related to curve fitting overview.
Fit function : fits curve or surface to data
So, fit function will return surface object or curve object. Here surface object is considered as sfit.
confint funtion: Confidence intervals for fit coefficients of cfit or sfit object.
confint(sfit) returns 95% confidence bounds.for sfit or cfit object.
Refer to this documents for better understanding :
So, sfit is not totally related to goodness of fit. MATLAB has function
goodnessOfFit which you can use.
goodnessOfFit : returns fit values that represent the error norm between test and reference data sets.
Refer to this document and example for better understanding :
  2 Commenti
andres
andres il 18 Giu 2020
Thanks for your answer, but this is not what I was meaning. I was not precise in my first message, my apologies for this.
When you run mrnfit https://nl.mathworks.com/help/stats/mnrfit.html one of the outputs is stats, and one of its components is sfit. I can't understand its meaning.
Nikhil Reddy Pottanigari
Nikhil Reddy Pottanigari il 19 Giu 2020
Hi,
Following context help you understand it, I think:
MNRFIT also returns parameter stats, which gives details about all the statistical information of the model. As
you have mentioned, the stats returned by mnrfit has parameter sfit. To the best of my knowledge, it
represents the error between the predicted and actual data, which is also a dispersion measure like Variance
and Root Mean Square Error.

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