Multivariable Logistic Regression in R: The Ultimate Masterclass (4K)!

[@ferhat5157]

Great video. Could you please record mixed (random) effects models as well. I know it’s a big ask but at least linear and logistic would be great!

[@yuzaR-Data-Science]

Of course! The mixed-models content will come in the near future. Just need to cover some other basic models and topics, which also will be useful to you I hope, and then I plan to cover most of the spectra of the mixed models beyond linear and logistic and present the best (in my opinion) packages for mixed-models. thus, please, stay tuned! Kind regards! Yury

[@Dhallager]

Another great video! My R output has become 1000% easier and better following your videos. Will you include interpretation and visualization of interactions also?

[@yuzaR-Data-Science]

hey man, thanks a lot for such a nice feedback! yes, I plan a video on interactions in logistic regression. stay tuned and if you think my content could be also helpful for someone you know, please share my videos with them :) cheers

[@hikeaway1596]

finally! waited for that video long time! thanks!

[@yuzaR-Data-Science]

Glad I did it! Took me a long time to make. Hopefully it’s useful!

[@MarcoBozzo-mj9uw]

man your presentation is staggering. keep doing your thing, do not lose an inch

[@yuzaR-Data-Science]

Thanks a ton, Marco 🙏 I’ll do my best to keep the content going 😉 hope you like other videos too. Kind regards

[@BonesFrielinghaus]

Like how you explain everything. And a clear, easy to understand voice (some none-1st Lang English speakers are SO MUCH WORK to understand - way too much cognitive load for me). You're easy to parse...and thanks for the non- YT generated caption text 💙💙💙

[@yuzaR-Data-Science]

Awesome! I’m really stoked you find it easy to understand! Makes all the work worth it! The subtitles were also suggested by my permament viewer. Thus, don't hesitate to suggest any improvements I can make for the video to increase the quality of content. Thanks for watching! 🙌

[@rubyamanda9009]

I really like how you are patient and make the interpretations so understandable! I also love the memes 😂 Please do you have a website where you share the codes? Please can you make a video explaining the basic assumptions, visualisations and interpretations of the outcomes from the nearest neighbour matching outcome?

[@yuzaR-Data-Science]

Thank you soo much for such a nice feedback! :) I am never sure, whether people like my memes, but I find similar memes in other videos always good :) the nearest neighbour matching outcome is actually new to me, I check that our and find it totally interesting. I'll put it on the list ;) Thanks for watching!

[@yurisilvadesouza3059]

The best explanation I have ever seen!

[@yuzaR-Data-Science]

Thank you so much for the kind words! Your support really motivates me to keep creating! If it helped you, please, share it with somebody, who also might benefit from it! That would mean the world to me! Cheers, Yury

[@edinsondelgado4895]

Please do a mixed effects model (random ) video! Your videos are the best on KZbin so far.

[@yuzaR-Data-Science]

thanks you so much! I'll definetely do videos on mixed models in R! Stay tuned ;)

[@Ange-y1k]

Thanks a lot for this piece of work 👌

[@yuzaR-Data-Science]

You are very welcome!

[@GreigR]

That's an amazing summary - thank you so much and yes please to a mixed effects and a linear model video

[@yuzaR-Data-Science]

Sure thing! They were on my radar anyway, but now I am getting serious about them! The content will come in the near future. Just need to cover some other basic models and topic, which also will be useful to you I hope, and then I plan to cover most of the spectra of the mixed models beyond linear and logistig and present the best (in my opinion) packages for mixed-models. thus, please, stay tuned! Kind regards! Yury

[@yuzaR-Data-Science]

Hi Greig, When you mean a usual linear regression (not mixed-effects linear), then I have recently done 4 videos on it. Besides, I have content on quantile, robust, bootstrapping regressions .... since I use them too in my everyday work life. Hope other videos will also resonate with you. And hope you'll stick around until I create a mixed-effects series ;) Kind regards! Yury

[@SUNILYADAV-tv5ze]

Nice lecture deliverd and Best explanation about multivariable logistic through example. Thanks

[@yuzaR-Data-Science]

Glad it was helpful! Thanks for watching!

[@warrenmalambo578]

Great video. Looking forward to a separate video on ROC curve and confusion matrix.

[@yuzaR-Data-Science]

Coming soon! Thanks for watching! :)

[@robertc2121]

Thank you so much. Love your content. This is incredibly helpful. I hope you do linear too :)

[@yuzaR-Data-Science]

thanks Robert! When you mean linear regression, then I have recently done 4 videos on it. Besides, I have content on quantile, robust, bootstrapping regressions .... since I use them too in my everyday work life. Hope other videos will also resonate with you. Kind regards! Yury

[@RaoniDominguesMD]

Great video! Please, can you make one on variable selection for multivariate models? Excellent content!

[@yuzaR-Data-Science]

:) I actually already did ;) check out my video on {glmulti} package and let me know whether it's what you wanted. Thanks for feedback and for watching!

[@aram5704]

You are a magician!

[@yuzaR-Data-Science]

Really appreciate your feedback 🙏 Thanks for watching!

[@wasafisafi612]

Thank you for the video

[@yuzaR-Data-Science]

You are very welcome 🙏

[@r.hainez2131]

That is another great video, thank you so much! For the ROC curve, the performance package provides a function which produces a similar result : performance_roc(x = m) %>% plot() . Is there a difference with pRoc::roc() ?

[@yuzaR-Data-Science]

Glad it was helpful! Sure, there are several functions for ROC curves in R. Several packages provide good results, but I like two of them more then the rest: Epi::ROC(form = survived ~ predicted_glm, data = d, plot = "ROC", grid = F, MX = T, MI = F, lwd = 3) cutpointr() - I am workind on a whole video about this one, it's just amazing

[@rcanjino]

Fantastic intro to a whole analysis pipeline for logistic regression. Do you have something similar for survival regression? ❤

[@yuzaR-Data-Science]

Unfortunately not. Only two older theoretical videos on survival, but they low quality and no programming. Plan to do the similar one in the future. So, please, stay tuned.

[@rcanjino]

@ looking forward to that. Thanks for this great vid nonetheless!

[@yuzaR-Data-Science]

welcome!

[@nikeforo2612]

Terrific video, very detailed yet clear. I don't know if you covered it already, but if you plan to cover cross-tabulation analysis, would you consider giving my 'chisquare' package a try?

[@yuzaR-Data-Science]

Hi Nike, thanks for the positive feedback. And I am interested in your 'chisquare' package. Unfortunately I did not find much info online on it. I have actually already made one video on chi-squared test. If you have seen this one, what does your package does better and differently? If you send me the code for what your package can do and explanations why it is useful and why it is better than usuals chi-square function or ggbarstats, I would love to make a video on your package!

[@nikeforo2612]

@@yuzaR-Data-Science Hello, and thanks for your reply. The package is on CRAN, and it's currently in its version 1.1.1 (it started from vers 0.1 in 2022). In few words, the package is meant to provide a one-stop shop for chi-square analysis of cross-tabs, and provides a number of facilities that are not coherently integrated in existing packages (to the best of my knowledge). For example, it provides (in just one simple line of code), different types of chi-sq residuals (with adjustements for multiple comparisons, and color coded for easy visual interpretation) and a extensive suite of association coefficients (for both 2x2 and larger tables), some of which not currently implemented elsewhere (maximum-corrected version of the phi and Cramer's V coeff, corrected version of Goodman-Kruskal's lambda, both asymmetric and symmetric). Also, it provides different versions of the chi-sq test itself, like the N-1-corrected version, which (again) is not currently provided elsewhere. As for post-hoc-analysis, it provides measures not currently available elsewhere, like the so-called Quetelet index and the IJ association factor. Further, it computes independent odds ratios for tables larger than 2x2, while for 2xK tables it can optionally produce a plot of pair-wise odds ratios (plus confidence intervals). Also, it provides suggestions as to a 'viable' chi-sq test given the input table characteristics. Effect size verbal articulation for relevant association coefficients (both chi-square-based and marginal-free) are also reported. Finally, all the outputs are nicely formatted via the 'gt' table package. I think that should be almost pretty much all. Everything can be obtained by just running: chisquare(mytable). Cheers.

[@yuzaR-Data-Science]

hey, your package is impressive, I found the visualization of odds ratios good. I have two questions: - first, do you have more info, like article or so on post hoc pairwise tests with all the significance, like when we have a table 4x4 or 3x5, so that all categories (percentages) are checked automatically. till now I use a pairwise_fishers_test() function which is cool, but an extra code. It would be amazing when we could just use your function and get all we need - ORs plot with significances and all the pairwise 2x2 tests from bigger contingency table in some form of a table. - second, may be more important: I could not get chisquare() function work with a simple table() function: > chisquare(table(mtcars$cyl, mtcars$am) ) Error in `gt::tab_style()`: ! Failed to style the body of the table. Caused by error in `cells_body()`: ! Can't select columns that don't exist. ✖ Column `0` doesn't exist. Run `rlang::last_trace()` to see where the error occurred. so, when this can be allowed and we could do bigger tables, like this one: chisquare(table(ISLR::Wage$jobclass, ISLR::Wage$education) ), this could be awesome!

[@nikeforo2612]

@@yuzaR-Data-Science Hello. Thanks for taking the time to check that and for replying. I do not want to hijack your comments section here. If you want to contact me on the email you find in the package documentation, I will more than happy to discuss things further. Looking forward. Cheers.

[@yuzaR-Data-Science]

hey mate, no worries, you don't hijack the comments section! :) I am actually glad to read and answer the comments. the next weeks I'll be on holidays, but we can talk about your package next month. generally, as I said before, I would love to be able to apply your chisquare function to a simple cross table, like that "chisquare(table(mtcars$cyl, mtcars$am) )". do you think it's possible?

[@123eorl]

amazing!!

[@yuzaR-Data-Science]

Thank you! Cheers!

[@joshstat8114]

Where (or when) could be the "Multivariate" Linear Regression one, since you covered the Multivariable Linear (this time, logistic) Regression?

[@yuzaR-Data-Science]

By multivariate you mean several outcomes? The terms is used often, but people define it differently.

[@joshstat8114]

@@yuzaR-Data-Science And I don't like that way. It should be defined equivalently. In that way, many literatures will be produced and reproduced.

[@yuzaR-Data-Science]

Oh man, the more I do science the more I see it's imperfections. Different definitions of the same think are the norm. Unfortunately. But still, I think, science is the best thing people can do.

[@GreenManXY]

I tried using the performance package on various models and unfortunately it seems a bit limited to lm and glm. Doesn't work with glmnet, for example. Doesn't work with KNN or RandomForest. I'm assuming it's because it checks for linear assumptions only... Bit of a shame, I had hoped it could be a go to tool for all model types. For now, I find that the parsnip package has more standardized functions like collect_metrics. But they're not as visually cool as check_model...

[@yuzaR-Data-Science]

well, yes, performance package doesn't work well with the machine learning models, but it works with almost all "important" statistical model, from frequentists to bayesian. I use more stats than ML, so I can't suggest an alternative better then collect_metrix at the moment. But I'll get into ML one day and will see what I'll find. In the meanwhile, I hope you enjoy the rest of the videos :) cheers