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@zeileis @fabitmart @rstats @latex Thanks for the plug Achim! Fabio, if you try modelsummary and run into issues, don't hesitate to ping me.

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@zeileis @fabitmart @rstats @latex Thanks for the plug Achim! Fabio, if you try modelsummary and run into issues, don't hesitate to ping me.

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@cameronpat I added examples to ?predictions and ?comparisons, as well as one of the vignettes: https://vincentarelbundock.github.io/m arginaleffects/articles/predictions.html#adjusted-predictions-at-user-specified-values-aka-adjusted-predictions-at-representative-values-apr

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@cameronpat I added examples to ?predictions and ?comparisons, as well as one of the vignettes: https://vincentarelbundock.github.io/m arginaleffects/articles/predictions.html#adjusted-predictions-at-user-specified-values-aka-adjusted-predictions-at-representative-values-apr

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@andrew @cameronpat That’s an interesting point. In my field, we often estimate models with factors that contain *many* levels, so using all unique values could yield massive output. Frankly, using the mode was more of a safety precaution than a principled choice. I can see a case for a different default, but feeding `unique` to datagrid() didn’t feel too costly either.

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@andrew @cameronpat That’s an interesting point. In my field, we often estimate models with factors that contain *many* levels, so using all unique values could yield massive output. Frankly, using the mode was more of a safety precaution than a principled choice. I can see a case for a different default, but feeding `unique` to datagrid() didn’t feel too costly either.

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@cameronpat @andrew the key insight is that {marginaleffects} nearly always behaves like the base R predict() function: it generates one estimate per row of the `newdata` argument. Then you can marginalize with `by` or `summary()`. In that sense, the package behaves in a really “standard” way

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@cameronpat @andrew the key insight is that {marginaleffects} nearly always behaves like the base R predict() function: it generates one estimate per row of the `newdata` argument. Then you can marginalize with `by` or `summary()`. In that sense, the package behaves in a really “standard” way

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@cameronpat @andrew Here's a new gist will code and comments inserted: https://gist.github.com/vincentarelbun dock/937f01d3dd20674e30492d4ae3f851c0

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@cameronpat @andrew Here's a new gist will code and comments inserted: https://gist.github.com/vincentarelbun dock/937f01d3dd20674e30492d4ae3f851c0

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@cameronpat @andrew @cameronpat @andrew To grok the mental model, I **strongly** recommend you start with the `predictions()` function. This function just makes predictions (fitted values) on the grid that you specify in the `newdata` arguement (very similar to base R predict()). Then, you can use the `by` argument to take averages (marginalize).

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@cameronpat @andrew @cameronpat @andrew To grok the mental model, I **strongly** recommend you start with the `predictions()` function. This function just makes predictions (fitted values) on the grid that you specify in the `newdata` arguement (very similar to base R predict()). Then, you can use the `by` argument to take averages (marginalize).

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@andrew @cameronpat Yeah, I don't know. Cameron might be talking about actual marginal MEANS and not marginal EFFECTS, in which case the answer is different. If he supplies a working example with the `mtcars` dataset and `emmeans` I can probably show how to replicate the results with `marginaleffects`.

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@andrew @cameronpat Yeah, I don't know. Cameron might be talking about actual marginal MEANS and not marginal EFFECTS, in which case the answer is different. If he supplies a working example with the `mtcars` dataset and `emmeans` I can probably show how to replicate the results with `marginaleffects`.

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@andrew @Miles_Ott @MineDogucu @cameronpat and some more side by side comparisons in the “Alternative software” and “Marginal means” vignettes.

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@andrew @Miles_Ott @MineDogucu @cameronpat and some more side by side comparisons in the “Alternative software” and “Marginal means” vignettes.

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@NathanKalmoe Exactly. I saw lots of people asking about which server they should sign up for, and then many people trying to be helpful by posting 20k word tutorials, when a simple “doesn’t really matter” could do the trick.

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@NathanKalmoe Exactly. I saw lots of people asking about which server they should sign up for, and then many people trying to be helpful by posting 20k word tutorials, when a simple “doesn’t really matter” could do the trick.

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@instrumenthull so much joy

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@instrumenthull so much joy

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@brodriguesco soooo I’d been a vim-in-tmux-Linux guy since 2006, but have been working in vscode with vim bindings inside a WSL instance from a windows machine for a few months. Sounds convoluted, but it’s basically zero-config, and I kind of like it. I feel like I’m losing nerd-cred, but maybe that’s Ok.

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@brodriguesco soooo I’d been a vim-in-tmux-Linux guy since 2006, but have been working in vscode with vim bindings inside a WSL instance from a windows machine for a few months. Sounds convoluted, but it’s basically zero-config, and I kind of like it. I feel like I’m losing nerd-cred, but maybe that’s Ok.

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@svmiller yeah. So far the move seems pretty painless. I used a website to scrape a bunch of follows from Twitter and it hit quite a few. And maybe starting smaller is a good thing.

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@svmiller yeah. So far the move seems pretty painless. I used a website to scrape a bunch of follows from Twitter and it hit quite a few. And maybe starting smaller is a good thing.

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@BenediktEhinger glad you think it might be useful. Don’t hesitate to ping me if you have feature requests.

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@BenediktEhinger glad you think it might be useful. Don’t hesitate to ping me if you have feature requests.

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{marginaleffects} is an easy-to-use📦to compute predictions, contrasts, slopes, and marginal means for 71 model classes in #RStats. v0.8.0 comes with powerful new features, bug fixes, and a website with 20k+ words of tutorials https://vincentarelbundock.github.io/m arginaleffects/

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{marginaleffects} is an easy-to-use📦to compute predictions, contrasts, slopes, and marginal means for 71 model classes in #RStats. v0.8.0 comes with powerful new features, bug fixes, and a website with 20k+ words of tutorials https://vincentarelbundock.github.io/m arginaleffects/

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@alexpghayes looks great! Bookmarked

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@alexpghayes looks great! Bookmarked

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@michaelflynn Looks fantastic! I’ll be working on some slides soon and might have to ask you a couple questions ;)

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@michaelflynn Looks fantastic! I’ll be working on some slides soon and might have to ask you a couple questions ;)

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@jayrobw @andrew @michaelflynn Thanks! Looks like you can move your followers as well, according to: https://docs.joinmastodon.org/user/mov ing/

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@jayrobw @andrew @michaelflynn Thanks! Looks like you can move your followers as well, according to: https://docs.joinmastodon.org/user/mov ing/

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@andrew @michaelflynn thanks, that's good to know! And if I accumulate some followers on my current server (fosstodon) but decide to move later to fediscience, then I "lose" all the followers, right? So my initial choice is very important...

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@andrew @michaelflynn thanks, that's good to know! And if I accumulate some followers on my current server (fosstodon) but decide to move later to fediscience, then I "lose" all the followers, right? So my initial choice is very important...

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@michaelflynn I don't quite understand the different communities work. I guess we can have a single timeline for all the people we follow across communities?

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@michaelflynn I don't quite understand the different communities work. I guess we can have a single timeline for all the people we follow across communities?