R demo | Robust Regression (don't depend on influential data)

Linear regression can be very sensitive to unusual data, like outliers, high leverage observations or a combination of both. A robust regression suppose to provide a solution for that. So, let's build both an ordinary and a robust regressions, compare them to find out whether outliers are a serious problem and see whether robust model performs better then usual linear model.

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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[buraktiras]

I’ve been away from statistical programming for months. Now I’m back and can’t express my gratitıde to check this channel again. One of the biggest source of knowledge if someone’s learning R. Thank you for everything.

[jamiyana]

Literally the best explanation out there! Amazing explanation in the simplest way and great video quality as well. A whole semester of lecture at uni only makes sense now. You're really saving our students life a lot

[WilForDataScience]

Hi Mr. Zablotski, it's me again. Great video!!! This is a kind of regression/ML that I didn't even know could exists. Thanks to this I am going to investigate more and study it in depth. In fact I am kind of "stealing" your code to apply it in my own R project 😀

PS: While studying with this video I could notice that in 01:41 observation #5 has the highest weight but not the smallest residual, Can you please explain it so I can get a better understanding? or maybe it is just me who didn't get the topic right: I am up to receive any corrections.

PS2: Would you consider creating a deeper video regarding influential observations (i. e. Outliers and Leverages) and how to treat/handle them?

PS3: Would you consider a PCA in R video?

That's it! Thank you very much sir, greatings from Colombia. 🤜🤛

[muhammedhadedy]

Excellent and brilliant, as usual. You are a true legend, my dear professor.
Just one question. Can I use this robust approach in logistic and Cox regression as well?
Thanks in advance.

[knutjagersberg]

You keep creating the very best #rstats content.

[hemantjoshi]

Thank you ! Can you have another tutorial posted on how to comprehend the outcome of robust regression. Thanks again !

[chacmool]

How is 'robustbase::lmrob()' different from 'ltsreg()' in base R? I note that both are iterative, potentially computationally expensive and non-convergent and binary in their weighting of observations.

[shaunaheron]

This was excellent--one question: can you recommend an R package for robust analyses of multilevel binomial models (glmer)? I had hoped that robustlmm could do the job, but unfortunately it only applies to gaussian distributions :( Thanks so much for all of your excellent videos!

[biologicalstatistics]

these are great packages. It would be good if we can get an idea on how to perform these procedures manually and without the use of extra packages.

[oluwafemioyedele]

Thank you, it was very useful, do you have any blog post on this ?

[fishfish]

As always, precise and impacting. I was thinking, can you do something on NonParametric methods of ordination and anova(permanova, anosim and mantel testfor Ecological/biological studies? Thank you.

[abdulmusa]

Thanks so much sir, this is very helpful. Sir, I am working with survey data would you recommend an R package to analyze multiple response variables in R?