minor question: shouldn’t nobs() return an integer for a GLM lm model?

```
julia> y= [1:10;] ; X= hcat( fill(1,10), y.^2, y.^3 );
julia> nobs( GLM.lm(X,y) )
10.0
```

are there cases where this can become a fraction?

minor question: shouldn’t nobs() return an integer for a GLM lm model?

```
julia> y= [1:10;] ; X= hcat( fill(1,10), y.^2, y.^3 );
julia> nobs( GLM.lm(X,y) )
10.0
```

are there cases where this can become a fraction?

Yes, when non-integer weights are provided. A solution to that would be to parameterize the model on the type of weights, and default to `Int`

when there are no weights.

thx. Simplicity is better. I would suggest an nobs() function that is truly the number of observations (and always integer) and a wobs() function that is the sum of the weights. should I suggest it as an issue on github?

Feel free to file an issue, but I’m afraid it’s more complex than that. For example, with frequency/replicate weights, the apparent “number of observations” doesn’t have any meaning, it’s just the way the data has been compressed to save space. So it would be misleading to have `nobs`

return that.

I would appreciate an option to return the number of rows used in a regression. It’s useful in regressions with missing values where you want to make sure you are getting the right subset of data each time.

It’s pretty easy to define a function though.

```
function unweightednobs(m) nrow(m.mf.df) end
```