Bonjour, comment faites-vous pour afficher un tableau des individus et leurs catégories comme à 13:16 sur votre vidéo ? J'ai cherché une solution de mon côté, ainsi que dans vos scripts, sans succès...
Thank you so much for your series of videos. Could you make available the codes that you used for making theses graphs and correlations? Thank you again.
Hello Francois, greetings from Chile. I have a query, I bought your book from amazon, since I am learning mca en r (very good book, congratulations). I am analyzing a survey and I want to replicate the graph that appears in the 15:00 minute, where the individuals are and you highlight 4 points (ID) on the diagonal. How do you do it? Do you have the code? I couldn't do it. I will be very grateful if you can share it since I could not find it in your notes. Cheers
Hi François. Thanks a lot for your videos, they're very helpful! I didn't quite understand why if the barycenter of a category is closer to the origin than another category of the same variable, then that implies that the first category contains fewer individuals than the second category. I'm following along with Kassambra's book PC methods and read that the farther a barycenter is to the origin, the less its quality/impact on the factor map. Would be very grateful if you could explain. Merci!
Hey François thanks for you videos great work, quick question is there a way to define and establish the relationships between the correlation ratio and the MCA. Thanks in advance.
The correlation ratio is the R-square of the model where Y are the coordinates of the individual on a dimension, and x is a qualitative variable. So a 1-way analysis of variance.
👋, I am using mca for my analysis and in the 2 dim I got only 19% of the total inertia explained, how will I justify to proceed in my analysis even the model isn't sufficient.
Everything is available on that website: husson.github.io/MOOC.html#AnaDoGB You will find videos, the slides and also the lines of code and the datasets.
Hi, thank you for the nice video. I have a question. I was wondered, whether MCA creates a K dimensional or a K-J dimensional point cloud? Because in the next video, you are talking about subspaces and I cannot find the relationship between these two. So, is it correct that MCA creates a K-J point cloud of individuals and an I dimensional point cloud of categories?
Bonjour, comment faites-vous pour afficher un tableau des individus et leurs catégories comme à 13:16 sur votre vidéo ? J'ai cherché une solution de mon côté, ainsi que dans vos scripts, sans succès...
Thank you so much for your series of videos. Could you make available the codes that you used for making theses graphs and correlations? Thank you again.
Hello Francois, greetings from Chile. I have a query, I bought your book from amazon, since I am learning mca en r (very good book, congratulations). I am analyzing a survey and I want to replicate the graph that appears in the 15:00 minute, where the individuals are and you highlight 4 points (ID) on the diagonal. How do you do it? Do you have the code? I couldn't do it. I will be very grateful if you can share it since I could not find it in your notes. Cheers
Hi François. Thanks a lot for your videos, they're very helpful! I didn't quite understand why if the barycenter of a category is closer to the origin than another category of the same variable, then that implies that the first category contains fewer individuals than the second category. I'm following along with Kassambra's book PC methods and read that the farther a barycenter is to the origin, the less its quality/impact on the factor map. Would be very grateful if you could explain.
Merci!
Hey François thanks for you videos great work, quick question is there a way to define and establish the relationships between the correlation ratio and the MCA. Thanks in advance.
The correlation ratio is the R-square of the model where Y are the coordinates of the individual on a dimension, and x is a qualitative variable. So a 1-way analysis of variance.
👋, I am using mca for my analysis and in the 2 dim I got only 19% of the total inertia explained, how will I justify to proceed in my analysis even the model isn't sufficient.
Amazing!!! Is it possible to share the codes you used to create the graphs? This would be really helpful 👍🏻
Everything is available on that website: husson.github.io/MOOC.html#AnaDoGB
You will find videos, the slides and also the lines of code and the datasets.
Hi, thank you for the nice video. I have a question.
I was wondered, whether MCA creates a K dimensional or a K-J dimensional point cloud? Because in the next video, you are talking about subspaces and I cannot find the relationship between these two. So, is it correct that MCA creates a K-J point cloud of individuals and an I dimensional point cloud of categories?
Yes you are right. MCA creates a K-J point cloud of individuals and an I dimensional point cloud of categories?
Thank you for your kind reply.
8:01 i loved