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jaycrick merged 1 commit into from
Mar 11, 2026
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Some amendments by Dennis to 03 and 04 #37

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7 changes: 3 additions & 4 deletions quarto_scripts/03-Example3-1and3-11Caes.qmd
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Expand Up @@ -213,8 +213,7 @@ The codebase is in Fortran, and is very fast.
We can use `glmnet` to fit the same multinomial model
by setting `lambda = 0` (giving no weight to the regularisation step)
to get the unregularized estimates.
If you look up `?glmnet`, pay attention to the multinomial parameterization,
since the returned coefficients are not on the same baseline coding as `multinom()`.
If you look up `?glmnet`, pay attention to what they said about symmetric multinomial model which has a different parametrization; [see the section on "Multinomial Regression" in their vignette](https://glmnet.stanford.edu/articles/glmnet.html#multinomial-regression-family-multinomial) for the precise form of this parametrization. Because of a different parametrization, the returned fitted coefficients won't be same as `multinom()`.

```{r}
#| label: glmnet-multinomial-fit
Expand Down Expand Up @@ -384,7 +383,7 @@ because it fits a wide variety of multivariate response regression models.
It is not as fast as `glmnet` or `nnet` for large datasets,
because it uses the Fisher scoring algorithm (like the book).

Is is good practice to
It is good practice to
[read the documentation](https://search.r-project.org/CRAN/refmans/VGAM/html/vglm.html)
for the `vglm()` function
with `?vglm` to understand the arguments and output structure.
Expand Down Expand Up @@ -638,4 +637,4 @@ for (pkg in packages) {
cat("- **", pkg, "**: ", cit_text, "\n\n", sep = "")
}
}
```
```
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