I would have loved to have found that vid like a week ago.
@KelsieBomke10 күн бұрын
Thank you so much for this! I have been trying for hours to figure this out!
@banethor17 күн бұрын
Gemini (Google's ChatGPT) brought me here!
@mikiallen773317 күн бұрын
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 ?
@karlaandreoli1986Ай бұрын
I just arrived here, and I have to say thank you soooo much for this video! You are very didactic Hugs from Brazil 🥰
@danilocarvalho6444Ай бұрын
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!
@user-ge6ee1sf5h2 ай бұрын
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?
@bobmandinyenya80802 ай бұрын
Thanks Chloe, how can I make the plot as the one you have at 3:06 minutes for the different species?
@charleydublin73042 ай бұрын
Great explanation, you have a talent for explaining complex things well !
@tylahmills64333 ай бұрын
super helpfuil! Would love a video showing how to compare models (and esp mixed effect models) for best fit!
@alcinaxavier36233 ай бұрын
What if I want to test interections (they were significant for Tukey test)? What commends should I write?
@diegogonzales59833 ай бұрын
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
@martinabautista3 ай бұрын
You are incredible! I enjoy every second I watch your video
@chacmool25813 ай бұрын
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.
@rubyanneolbinado953 ай бұрын
Hi, why is R studio producing different results even though I am using the same call and data.
@chloefouilloux3 ай бұрын
Hmmmmm, I wouldn't know without looking at your code, but you can check out the code of this video that I have annotated on my GitHub to see if there are any mismatches. github.com/chloefouilloux/GLMOutput/blob/main/GLM_Output.Rmd
@yuvalgal-shahaf27823 ай бұрын
You manage to make statistics fun anc cool! wow. Thank you so much. You are great
@flovromvrom10044 ай бұрын
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
@rubyanneolbinado954 ай бұрын
thank you for the information.
@chloefouilloux4 ай бұрын
Thanks for the feedback 😸 I'm working on a follow-up video that might include interactions and other model families. If it's okay could you let me know what info you felt was lacking? I'm always trying to improve on explanations!
@ALIENZHUMAN4 ай бұрын
🤐🤐🤐🤐🤐🤐🤐
@rubyanneolbinado954 ай бұрын
@@chloefouilloux ohh thank you so much for the prompt reply. I am just frustrated and confused on how to select the best model for my 7 response variables. Should I use the AIC (via backward selection) to select the best fitted model or should I just use 3 models (of which I selected the explanatory variables, one with only 2, one with 5 and one with 5 explanatory variables+interactions). Please help me what should I do on this. I've done too many researches but they have used different methods and just confused me more. Huhu
@rubyanneolbinado954 ай бұрын
@@chloefouilloux one more things please. Is it okay to use just one model for my different 7 response variables?
@chloefouilloux4 ай бұрын
@@rubyanneolbinado95 Hi hi! Okay, let me tackle these one at a time. (1) One glm model for 7 predictors is probably not going to be great (especially if there are interactions!). These models tend to be *overfit* which means that you are trying to split your data into too many little boxes-- fewer predictors means more explanatory power (check dharma part of the video-- you can check dispersion of your model using dharma too!). (2) So, how to reduce the number of predictors? Well, you can do the backward selection that you mention, for sure. I don't love to use this method *initially* because it can get rid of the variables you are actually interested in! (because stepwise isn't a biologist, you are!). I would first check if any of your predictors are collinear/autocorrelated! (ex. mass and length are two variables that often are highly correlated-- when you have too much autocorrelation between predictors, they get mad at each other and wreck your model) -- here, you can check correlation between variables *and choose which one is more biologically reasonable* to keep in the model-- drop the other ones. (3) If option 2 isn't working out for you, a GLM just might not be the right model for your data! I would start thinking about a PCA or more advanced modelling, like mixed models. Hope this helps :)
@keniadanielareyesochoa12244 ай бұрын
OMG, this is pure gold! Thank you so much <3
@charlottemcwilliams29094 ай бұрын
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!
@livinglyrics27784 ай бұрын
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 KZread/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!! 🙌
@emilybrayton44574 ай бұрын
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!!!
@HerpetologoTropical4 ай бұрын
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.
@woodmorgn4 ай бұрын
antarctic research student here! Super helpful and interesting video!!
@philippjeske1634 ай бұрын
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
@jsc06255 ай бұрын
This really helped me fill in some knowledge gaps I had about the GLM, thanks so much 😊
@zackthompson62865 ай бұрын
Super useful that mutation tho!
@zackthompson62865 ай бұрын
You are hot
@Taricus5 ай бұрын
No no no... I say to have at least 30 in each group.... If the data is sparse, it can cause problems...
@chloefouilloux5 ай бұрын
I mean that is definitely better! But man oh man, try recapturing bank voles in the middle of an enormous forest over a summer. They are tiny little jerks!!!
@Taricus5 ай бұрын
The thumbnail tho!!!! LOL why did you have to make me feel old?! LOL!
@xavharrison90995 ай бұрын
I recognise that mixed effect model schematic on the thumbnail from Silk, Harrison & Hodgson 2020 PeerJ
@chloefouilloux5 ай бұрын
Yes! Beautifully written by some great researchers ;) I linked the paper in my GitHub, but added it now to the video description as well!
@KnorpelDelux5 ай бұрын
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 🙃
@user-ro9ex5im2p5 ай бұрын
Thanks!
@icefunkdark85555 ай бұрын
I love how you present it :) thank you!
@malaakallawati30616 ай бұрын
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.
@user-xy5ko8xr9i6 ай бұрын
chloe ily this is such a good video
@NeonoriNori6 ай бұрын
We have such fancy frogs here in my place. I saw it only once, Gree, clean black eyes, long lengs and red feet.
@gavinaustin44746 ай бұрын
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.
@arlindogomesfilho23707 ай бұрын
Excellent content and presentation... thanks a lot for that!
@LEQN7 ай бұрын
I really enjoy the goofy nature of your videos, keep making them if you get the chance!
@gentiangashi30227 ай бұрын
Your frustration with fitting quadratic functions on linear data got me 🤣
@user-mh7px2uy1k7 ай бұрын
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.
@user-mh7px2uy1k7 ай бұрын
Very good explanation, helpful reminder. And appreciate the tip on the Dharma package.
@user-nv6fq7qb1n7 ай бұрын
This was really helpfull, clear, and fun to watch ! thank you very much :)
@paulobarrosbio8 ай бұрын
Thank you! Amazing explanation! Really helped me understand key aspects of a GLM. And thanks to the tip on the DHARMa package!
@markelov9 ай бұрын
Loved your video! Have you ever used check_model() from the performance() package?
@chloefouilloux9 ай бұрын
I haven't! I just looked it up and it looks pretty cool. It seems very similar to DHARMa but perhaps a bit more flexible, which can be good or bad depending on your handling on stats (for example, I see that you can compare models with different parameters from different datasets within the same call! that seems. . . dangerous. . .and can be super misleading if you don't know what is underlying the output).
@markelov9 ай бұрын
For sure! I am slowly but surely making the transition to R by way of SPSS and then Stata, and am constantly amazed at how flexible R can be-for better or for worse! I have only tinkered with check_model(). I like that it offers a vehicle to visually inspect the most salient OLS assumptions at once, and especially love the added guidance of what you should be looking for to guide your interpretation. Merci mille fois !
@Hamromerochannel10 ай бұрын
Hi Chloe what’s your background (profession) ? Academics or …. ???
@chloefouilloux10 ай бұрын
Hi! I am in academia, yes! Which is why the videos are quite irregular, but I am going to try to get one up before the holidays!
@agustinabayon188710 ай бұрын
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?
@gabrielbatista432910 ай бұрын
Super helpful, what model would use for data that is not normally distributed?
Пікірлер
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.
Hmmmmm, I wouldn't know without looking at your code, but you can check out the code of this video that I have annotated on my GitHub to see if there are any mismatches. github.com/chloefouilloux/GLMOutput/blob/main/GLM_Output.Rmd
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.
Thanks for the feedback 😸 I'm working on a follow-up video that might include interactions and other model families. If it's okay could you let me know what info you felt was lacking? I'm always trying to improve on explanations!
🤐🤐🤐🤐🤐🤐🤐
@@chloefouilloux ohh thank you so much for the prompt reply. I am just frustrated and confused on how to select the best model for my 7 response variables. Should I use the AIC (via backward selection) to select the best fitted model or should I just use 3 models (of which I selected the explanatory variables, one with only 2, one with 5 and one with 5 explanatory variables+interactions). Please help me what should I do on this. I've done too many researches but they have used different methods and just confused me more. Huhu
@@chloefouilloux one more things please. Is it okay to use just one model for my different 7 response variables?
@@rubyanneolbinado95 Hi hi! Okay, let me tackle these one at a time. (1) One glm model for 7 predictors is probably not going to be great (especially if there are interactions!). These models tend to be *overfit* which means that you are trying to split your data into too many little boxes-- fewer predictors means more explanatory power (check dharma part of the video-- you can check dispersion of your model using dharma too!). (2) So, how to reduce the number of predictors? Well, you can do the backward selection that you mention, for sure. I don't love to use this method *initially* because it can get rid of the variables you are actually interested in! (because stepwise isn't a biologist, you are!). I would first check if any of your predictors are collinear/autocorrelated! (ex. mass and length are two variables that often are highly correlated-- when you have too much autocorrelation between predictors, they get mad at each other and wreck your model) -- here, you can check correlation between variables *and choose which one is more biologically reasonable* to keep in the model-- drop the other ones. (3) If option 2 isn't working out for you, a GLM just might not be the right model for your data! I would start thinking about a PCA or more advanced modelling, like mixed models. Hope this helps :)
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 KZread/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...
I mean that is definitely better! But man oh man, try recapturing bank voles in the middle of an enormous forest over a summer. They are tiny little jerks!!!
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
Yes! Beautifully written by some great researchers ;) I linked the paper in my GitHub, but added it now to the video description as well!
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?
I haven't! I just looked it up and it looks pretty cool. It seems very similar to DHARMa but perhaps a bit more flexible, which can be good or bad depending on your handling on stats (for example, I see that you can compare models with different parameters from different datasets within the same call! that seems. . . dangerous. . .and can be super misleading if you don't know what is underlying the output).
For sure! I am slowly but surely making the transition to R by way of SPSS and then Stata, and am constantly amazed at how flexible R can be-for better or for worse! I have only tinkered with check_model(). I like that it offers a vehicle to visually inspect the most salient OLS assumptions at once, and especially love the added guidance of what you should be looking for to guide your interpretation. Merci mille fois !
Hi Chloe what’s your background (profession) ? Academics or …. ???
Hi! I am in academia, yes! Which is why the videos are quite irregular, but I am going to try to get one up before the holidays!
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!