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I would have loved to have found that vid like a week ago.

Thank you so much for this! I have been trying for hours to figure this out!

Gemini (Google's ChatGPT) brought me here!

Bonsoir Cholea, merci pour tel introduction , neamoins , je voudrais bien savoir si la meme API key peut etre utilisee pour d'autres buts par example pour collecter des reviews /Bewertungen sur un produit ou bien un service ?

I just arrived here, and I have to say thank you soooo much for this video! You are very didactic Hugs from Brazil 🥰

Absolutely amazing! thanks so much! Another request, what to do with data that has **a lot** of zeros? Please keep posting videos like this; you are indeed amazing at explaining what is going on and what we should be looking for! Thanks!

Hello! Thank you for the video! May I ask to explain in details what Estimates mean in GLM please? Or where can I read more about it?

Thanks Chloe, how can I make the plot as the one you have at 3:06 minutes for the different species?

Great explanation, you have a talent for explaining complex things well !

super helpfuil! Would love a video showing how to compare models (and esp mixed effect models) for best fit!

What if I want to test interections (they were significant for Tukey test)? What commends should I write?

Hi, thanks for the explanation of the GLMM analysis. I have a question: Do you applied DHARMA::simulateResiduals after created the last model? Thanks :D

You are incredible! I enjoy every second I watch your video

Statisticians like to generalize and GLM is a generalization of lots of survival cases. For example, OLS regression is a surgical case of a GLM with a Gaussian link. Fit an lm() and a Gaussian GLM, and you'll get identical results.

Hi, why is R studio producing different results even though I am using the same call and data.

You manage to make statistics fun anc cool! wow. Thank you so much. You are great

That's a great video about the interpretation of the GLMs, but I'm confused because according to some references GLMs do not assume that you have the same variance within groups or while your continuous variables grown right? This is because it uses the log likelihood to estimate the parameters of the model to model the mean value. online.stat.psu.edu/stat504/lesson/6/6.1 also in the book Categorical Data Analysis, Agresti

thank you for the information.

OMG, this is pure gold! Thank you so much <3

Can you do a video explaining what family of curve to choose? Here you have picked gaussian each time. I am struggling with this step in my own glmm's. Thanks for all your help so far, this was an interesting example!

This video is the first video of yours that I’ve come across and I just wanted to say, I absolutely love your teaching and presentation style!! Your enthusiasm and explanation style are so engaging, it’s awesome; and, the way you break things down whilst also simplifying concepts is great, especially because such concepts are generally taught/explained in a much more complex way in university courses, textbooks, and in other UA-cam/online tutorials - together, I feel this all really helps with improving understanding of all concepts discussed. I’m a postgrad student and would have loved to have access to this type of content in my earlier years when learning stats - I must say though, I’ve still learnt some new info from this tutorial!! Would love to see more R programming tutorials like this one - if you’re thinking about posting more, please do because you definitely have the gift of making stats engaging and fun (descriptive words that you don’t usually find when people are talking about stats 😅). Thanks for this content!! 🙌

Thanks so much for this video, I feel like I have some clarity in understanding GLMs and my outputs so much more now. It feels good to have this confidence!!!

Dear Chloe, thank you very much for take your time to give us this tutorial. I´m new in RStudio and I really enjoy seeing all the graphs and analysis posibilities it offers. New subscriber.

antarctic research student here! Super helpful and interesting video!!

Very informative video with a refreshing amount of humour (which is rare to find in the world of statistics). Never had so much fun watchin this kind of videos, congrats haha! I would love to see more videos on GLMMs cause its cool but also complicated af

This really helped me fill in some knowledge gaps I had about the GLM, thanks so much 😊

Super useful that mutation tho!

You are hot

No no no... I say to have at least 30 in each group.... If the data is sparse, it can cause problems...

The thumbnail tho!!!! LOL why did you have to make me feel old?! LOL!

I recognise that mixed effect model schematic on the thumbnail from Silk, Harrison & Hodgson 2020 PeerJ

Very specific but should you have expertise in setting up MaxEnt with SWD files to run several projections out of the same model...videos would be highly appreciated 🙃

Thanks!

I love how you present it :) thank you!

This was super useful and helpful - shows how to approach different questions/situations. I was wondering how you learned R and if there was any particular source that you found super useful? I'm a university student and I enjoy using R very much but unfortunately did not have many modules that explored it in a more advanced way.

chloe ily this is such a good video

We have such fancy frogs here in my place. I saw it only once, Gree, clean black eyes, long lengs and red feet.

Hi Chloe, I was curious about the plot at 03:57. Given that you've got one categorical predictor with three levels (i.e., species) in your model, I wanted to know what the x-axis of this plot was. Presumably, this axis is species, but then why are the values not in three vertical clusters like in your half-eye plot? I thought it might have been because, in the plot at 03:57, you jittered the values associated with each level of the predictor variable; however, the values are more spread out horizontally for that. So I downloaded the files from your github site so that I could see what code you used for the plot at 03:57. However, unless I'm mistaken, the code for the plot at 03:57 wasn't in the markdown file. Also, when I ran the markdown file, the code crashed at this line: testDispersion(mod2a). I assume that this is because mod2a was not defined earlier in the code.

Excellent content and presentation... thanks a lot for that!

I really enjoy the goofy nature of your videos, keep making them if you get the chance!

Your frustration with fitting quadratic functions on linear data got me 🤣

I am learning mixed effect linear models - could you do a video on how to interpret the outcome of those types of models? I have tons of info on the modeling aspect but not entirely sure how to leverage the output effectively. I appreciate the humor and thoughtfulness in your videos to make them interesting.

Very good explanation, helpful reminder. And appreciate the tip on the Dharma package.

This was really helpfull, clear, and fun to watch ! thank you very much :)

Thank you! Amazing explanation! Really helped me understand key aspects of a GLM. And thanks to the tip on the DHARMa package!

Loved your video! Have you ever used check_model() from the performance() package?

Hi Chloe what’s your background (profession) ? Academics or …. ???

Great explanation! thank you so much for the video. Could you please make a video about which glm models can be used when the data is not normally distributed?

Super helpful, what model would use for data that is not normally distributed?

Thank you so much Chloe!