Understanding the glm family argument (in R)

The goal of this video is to help you better understand the 'error distribution' and 'link function' in Generalized Linear Models.

[Insipidityy]

THIS video really made GLMs "click" for me. I spent hours trying to figure out what do link functions and families mean, and I have found no better explanation. Thank you so much!

[joshuavernontanner]

I'm floored by how clear this was. Incredible teaching ability, thank you!

[Roy-xrwq]

Best Explanation, the visuals bring the whole idea into life. Thanks

[ibntuahirabdulhaqq]

this is not the first I am watching this video and any time I do I wish u made them a series of videos. Thanks for giving the best explanation and taking the time to respond to queries in the comment section. Much love

[os]

Awesome work! thanks! Cheers from a PhD Neurobiologist candidate from Colombia.

[laure]

Absolutely fantastic video! Thank you SO much! And I truly appreciated the little recap on error distribution as well, extremely helpful!

[davidgao]

very clear layout and superb explanation for the intuition. Thanks!

[gergerger]

Very well put together. I think there should be some recognition of the fact some of the symbols are mixed up in the presentation. The systematic component should always be mu and mu goes into the link function to give eta and eta is the value that goes into the random component distribution. Otherwise the slides don't make sense. To take a random example, the probit regression slide, mu is not defined anywhere. But changing systematic component to mu and then changing binomial parameter to eta then fixes everything.

[yahiarafik]

Very clear, thank you so much for this explanation, you just helped a lot of people in my major :))

[JinaneJouni]

Really well explained! Thank you very much

[dulquerpauly]

You absolute legend thank you mate.... Subscribed already :-)

[jessicahough]

Hey! I am a master student looking to make a GLM incl. random error. I find that both my dependent and independant variables (all continuous) do not follow any of the distributions. I also don't know if I should then try and transform them before putting it into this model? Also, what part of my data needs to follow these distributions? Independant or dependant variables? What if after I transform them, some follow poisson (eventhough they are not discrete integers) and others follow a normal distribution after I add log? Shall I admit defeat?

[yiyuanzhang]

thank you very much! however, it is still unclear for me when should i use which family and which link function. should i check how error terms distributed and then decide which family and link function to use?

[darmaw]

I'd really appreciate it if you could let me know how you managed to have a list of arguments shown instead of lines. Many thanks!

[djangoworldwide]

Nice video but at the end to state "identity" without explaining about the fact it's the I matrix, is a bit lacking