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@phdskat Fascinating! I guess I'm fundamentally a technophile, so while I'm somewhat unsettled by all this, my first inclination is to think it would be a shame to reject potentially massive benefits out of hand. I'll be very interested to what people smarter than me think about all this.

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@phdskat Fascinating! I guess I'm fundamentally a technophile, so while I'm somewhat unsettled by all this, my first inclination is to think it would be a shame to reject potentially massive benefits out of hand. I'll be very interested to what people smarter than me think about all this.

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@phdskat Also of writing more generally. Go from outline to full paper in 2 seconds, then tweak and add tables ;)

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@phdskat Also of writing more generally. Go from outline to full paper in 2 seconds, then tweak and add tables ;)

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code ligatures and arrow assignment, like an animal

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@conjugateprior @strengejacke @andrew @kristoffer Oh right, I forgot about that.

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@conjugateprior @strengejacke @andrew @kristoffer Oh right, I forgot about that.

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@strengejacke @andrew @kristoffer Look at me accusing a TOOOOT of being "simplistic" 😂

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@strengejacke @andrew @kristoffer Look at me accusing a TOOOOT of being "simplistic" 😂

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@strengejacke @andrew @kristoffer This is too simplistic. It is very easy to cook up examples with omitted variable bias where mixed models perform much worse.

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@strengejacke @andrew @kristoffer This is too simplistic. It is very easy to cook up examples with omitted variable bias where mixed models perform much worse.

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The plotting functions in {marginaleffects}📦0.8.1 for #RStats are now much more flexible. They give users more control over the exact predictions, slopes, or contrasts to plot. You can now specify values manually, use functions, or one of many shortcuts. https://vincentarelbundock.github.io/m arginaleffects/articles/plot.html

# orderedItems[].object.contentMap.en
The plotting functions in {marginaleffects}📦0.8.1 for #RStats are now much more flexible. They give users more control over the exact predictions, slopes, or contrasts to plot. You can now specify values manually, use functions, or one of many shortcuts. https://vincentarelbundock.github.io/m arginaleffects/articles/plot.html

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@Norcalbiostat I wrote a whole vignette on Standard Errors and Confidence Intervals if you are interested in the details: https://vincentarelbundock.github.io/m arginaleffects/articles/sandwich.html

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@Norcalbiostat I wrote a whole vignette on Standard Errors and Confidence Intervals if you are interested in the details: https://vincentarelbundock.github.io/m arginaleffects/articles/sandwich.html

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@Norcalbiostat Sure, but that's irrelevant for standard errors, where we need: Var(E[Y_1-Y_0])=Var(E[Y_1] - E[Y_0]), and {marginaleffects} does the correct calculation under the hood.

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@Norcalbiostat Sure, but that's irrelevant for standard errors, where we need: Var(E[Y_1-Y_0])=Var(E[Y_1] - E[Y_0]), and {marginaleffects} does the correct calculation under the hood.

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@Norcalbiostat Same thing. E[X+Y]=E[X]+E[Y].

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@Norcalbiostat Same thing. E[X+Y]=E[X]+E[Y].

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@Norcalbiostat I think if you read the Contrasts/Comparisons vignette, you’ll find that comparisons() and then summary() is what you need.

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@Norcalbiostat I think if you read the Contrasts/Comparisons vignette, you’ll find that comparisons() and then summary() is what you need.

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@Norcalbiostat But if your counterfactual is defined as a +1 increase in a continuous regressor, then what you propose is exactly what the comparisons() function will do.

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@Norcalbiostat But if your counterfactual is defined as a +1 increase in a continuous regressor, then what you propose is exactly what the comparisons() function will do.

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@Norcalbiostat Say X is binary, what value of X generated Yhat_cf? You just flip the switch for every observation? If you do that, then you’ll be averaging unit level effects of increases from 0 to 1 with unit level effects of decreases from 1 to 0, which would cancel out. Normally, what you really want is the average difference on “potential outcomes”: mean(Yhat_{X=1}-Yhat_{X=0})

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@Norcalbiostat Say X is binary, what value of X generated Yhat_cf? You just flip the switch for every observation? If you do that, then you’ll be averaging unit level effects of increases from 0 to 1 with unit level effects of decreases from 1 to 0, which would cancel out. Normally, what you really want is the average difference on “potential outcomes”: mean(Yhat_{X=1}-Yhat_{X=0})

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@brodriguesco yeah, absolutely fantastic! And the programmability is so useful.

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@brodriguesco yeah, absolutely fantastic! And the programmability is so useful.

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@brodriguesco same switches, but moonlander. love it

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@brodriguesco same switches, but moonlander. love it

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@jmquinn There are 20,000k+ words of vignettes on the website, showing detailed walkthroughs with real data for each of the quantities of interest. Let me know if you read those and are still missing something. You may find the "Alternative Software" vignette particularly interesting, since it makes a bunch of explicit comparisons. Table of contents: https://vincentarelbundock.github.io/m arginaleffects/index.html#table-of-contents

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@jmquinn There are 20,000k+ words of vignettes on the website, showing detailed walkthroughs with real data for each of the quantities of interest. Let me know if you read those and are still missing something. You may find the "Alternative Software" vignette particularly interesting, since it makes a bunch of explicit comparisons. Table of contents: https://vincentarelbundock.github.io/m arginaleffects/index.html#table-of-contents

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@jmquinn I meant that in the context of this package a "marginal effect" is defined as a "slope". Other packages use the same expression to refer to what I call "adjusted predictions" or change in adjusted predictions ("contrast"). So in effect, they do *not* compute slopes.

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@jmquinn I meant that in the context of this package a "marginal effect" is defined as a "slope". Other packages use the same expression to refer to what I call "adjusted predictions" or change in adjusted predictions ("contrast"). So in effect, they do *not* compute slopes.

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@andrew Glad you solved it, but that 𝚞𝚗𝚌𝚕𝚊𝚜𝚜() trick is weeiiird.

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@andrew Glad you solved it, but that 𝚞𝚗𝚌𝚕𝚊𝚜𝚜() trick is weeiiird.