Hey there loving the look of this channel, just came across it, any recommendations where to begin? (Undergrad biochemist with next to no coding experience)
Hi, welcome! I'm also a biologist by training and self-taught myself coding. If starting out, I would recommend this playlist that shows a no code approach to using machine learning ruclips.net/video/t5mylGHE2Fg/видео.html I've also compiled a beginner friendly playlist for getting started in data science here ruclips.net/video/oR670Txwh88/видео.html
May I ask what is the basis behind the imputation calculation, or in other words, would there be issues with using imputation methods if the underlying distribution of the variable with missing values is uncertain or non-normal?
Hey Jeffrey, this depends on the imputation method you are using. In the video, I have used Predictive Mean Matching (the default of the mice package for numerical data), and with this method it shouldn't be a problem in case the imputed variable is non-normal. You can find more information on Predictive Mean Matching here: statisticsglobe.com/predictive-mean-matching-imputation-method/ However, a big concern of missing data imputation is the structure of the missing values (i.e. the response mechanisms MCAR, MAR, and MNAR). You can read more about that by following the link in the description of the video. I hope that helps! Joachim
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Thanks a lot Chanin for this amazing collaboration! It's an honor to be featured on your channel!
Good lecture sir ❤️
@@krishna9011 Thanks a lot, glad you think so! :)
Glad to have you, and thanks for sharing your knowledge with us!
Thank you for introducting us to mice package!
You are very welcome Gabriel, glad you find the tutorial helpful!
Great topic from two great presenters. I'm learning R and I regularly find great info on Joachim's StatisticsGlobe channel and website.
Love this! We were just discussing how to deal with missing data the other day on our show! Great to see how to handle it with R.
Thanks Albert for tuning in! 😊
Hey Albert, thanks a lot for the nice comment! Glad to hear that the tutorial came at the right time :D Regards, Joachim
Great tutorial
Thanks for watching!
Thank you very much Gulab, glad you like the tutorial! :)
Hey there loving the look of this channel, just came across it, any recommendations where to begin? (Undergrad biochemist with next to no coding experience)
Hi, welcome! I'm also a biologist by training and self-taught myself coding. If starting out, I would recommend this playlist that shows a no code approach to using machine learning ruclips.net/video/t5mylGHE2Fg/видео.html
I've also compiled a beginner friendly playlist for getting started in data science here ruclips.net/video/oR670Txwh88/видео.html
🔥🔥❤️
Thanks Ranit! :D
May I ask what is the basis behind the imputation calculation, or in other words, would there be issues with using imputation methods if the underlying distribution of the variable with missing values is uncertain or non-normal?
Hey Jeffrey, this depends on the imputation method you are using. In the video, I have used Predictive Mean Matching (the default of the mice package for numerical data), and with this method it shouldn't be a problem in case the imputed variable is non-normal. You can find more information on Predictive Mean Matching here: statisticsglobe.com/predictive-mean-matching-imputation-method/ However, a big concern of missing data imputation is the structure of the missing values (i.e. the response mechanisms MCAR, MAR, and MNAR). You can read more about that by following the link in the description of the video. I hope that helps! Joachim
Hi sir plz
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Thanks for the suggestion, let me see what I can do.
@@DataProfessor thanx for
Replying me in your busy schedule sir
I know it's not uncommon but having the multiple imputation be further off from the true y than the single was really anticlimatic.