R demo | Many (Grouped / Nested) Models Simultaneously are Very Effective

In this video, we'll learn how to produce grouped / nested models, whith an amazing "map()" function from {purrr} package in R. We'll use linear models in this example for the sake of simplicity, but you can apply any model you want (robust, logistic, poisson etc.). We'll see, how to effectively store and use the information from multiple models. And while in the video we'll produce "only" 10 models, that could be any number of models you want.
If you only want the code (or want to support me), consider join the channel (join button below any of the videos), because I provide the code upon members requests.
Enjoy! 🥳
Welcome to my VLOG! My name is Yury Zablotski & I love to use R for Data Science = "yuzaR Data Science" ;)
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[@alincastillo5203]

Please continue with more videos of this type, it is very useful for the entire community. Excellent video, thank you very much for the continuous contribution.

[@yuzaR-Data-Science]

Waw, thanks, Alin! That means the world to me! And, of course motivate to create more useful content! Thank you for watching!

[@oluwafemioyedele]

Another killer video using the power of purrr. Thank you!!!

[@yuzaR-Data-Science]

you are very welcome! thanks for your continuous support!

[@mkklindhardt]

I love your videos and screencasts! They are very educational and still very high-level.
Could you please make a video on performing meta-analysis in R using for example the metafor or other packages? Especially covering the choices one has to make concerning types of meta-analysis: e.g. fixed-effects, random-effects or bayesian meta-analysis of different types of response variables, modulators and outcomes. It could also be cool if you covered the considerations of multivariate and multilevel meta-analysis as well as composite outcome meta-analysis, such as the concept of "borrowing of strength" (BoS) in meta-analysis when dealing with multiple outcomes or studies with small sample sizes.

[@yuzaR-Data-Science]

Thanks for such a lovely feedback! The meta-analysis videos are definetely on my list! And they will be done. But the list still has a few topics before it, like all kind of models. Thus, please, don't expect them too soon, but stay tuned as they are important for my work and will be produced in the future!

[@joaoschneider4822]

I love these videos! Best R RUclips channel out there ♥️

[@yuzaR-Data-Science]

Wow 🤩 thanks a ton mate! That means a lot for me!

[@hemantjoshi5034]

I agree to your statement !

[@fishfish20]

Even when I'm tired, I can't enough of your content. GOAT Status in deed. Thank you sir.

[@yuzaR-Data-Science]

That means a lot to me!!! 🙏 I truly appreciate that, Jonathan!

[@abdulmusa6162]

Excellent work sir
Many thanks for bringing this significant concept

[@yuzaR-Data-Science]

You are most welcome! Thanks for the feedback, Abdul!

[@caduguimaraes]

Fantástic. Thanks so much

[@yuzaR-Data-Science]

Glad you enjoyed it! Thanks for watching!

[@yuzaR-Data-Science]

Glad you enjoyed it! Thanks for watching!

[@buffaloperformanceandanaly1431]

This is a great video! Thank you for explaining it so clearly

[@yuzaR-Data-Science]

You are very welcome! Thanks for watching!

[@buffaloperformanceandanaly1431]

@@yuzaR-Data-Science Is it possible to run nested linear models when the data is stored in a data frame? 🤔

[@alelust7170]

tks a lot! 😃

[@yuzaR-Data-Science]

Welcome mate!

[@knutjagersberg381]

Great video, again!

[@yuzaR-Data-Science]

Thanks again! I am very grad it's useful! :)

[@biologicalstatistics3320]

great show of using many different packages working together. it would be a great addition if the plots are all consistent (themes) as well.

[@yuzaR-Data-Science]

oh yeah, I probably forgot to set_theme( ... ) to one theme. thanks for the feedback and for watching!

[@siriyakcr]

Thanks a lot 🥰🥰,

[@yuzaR-Data-Science]

You are very welcome 🙏

[@danjarupath]

Excellent 👍👍👍👍

[@yuzaR-Data-Science]

Thank you! You might like the bootstrapping video too. It's my recent. Cheers!

[@luizclaudiolouzada8741]

além de agrupar os modelos por "health_ins" combina também com "jobclass"? Grato!

[@yuzaR-Data-Science]

feliz que você gostou! obrigado por assistir!