Linear mixed effects models 

Oh my goodness, thankyou for making a video that actually explains statistical content clearly! If I had a dollar for every video with a title like, "such and such analysis method, CLEARLY EXPLAINED!" then goes on to dive into the most complex content imaginable without proper explanation I'd be a very rich man. Sorry about this vent, I'm just very appreciative. Keep up the good work.

By far the best explanation on LMM. Thanks

Great explanation man, I really appreciate the effort! Although there is a lot of information available and also a lot of sources where to find them, it takes a lot of effort to explain these kind of models graphically. I've read about these models from 2 or 3 different sources in order to get a general picture, but this one is a nice and clear explanation, besides been shown as figures

I am new to this model and I have to say that this video is really helpful! Thanks!

Studying psychology and this was super helpful!! Thanks

This was incredibly helpful. Thank you!

this is the best explanation I saw so far! thank you so much!

Thank you for sharing this complete explanation of LMEM! Super helpful!

thank you so much! this is so helpful and you are great at explaining.

Thanks Matthew. Very good explanation.

Great video, thanks!! Just enough information to get me started without going into full-blown detail.

Thank you sir! Even with this simple explanation the topic is still complicated. I wished the examples were simpler.

Really good explanation! Helping me write my first manuscript :)

This was really helpful. Saved my day!!

thanks! very clear visualisations

Fantastic video! Thank you so much! You are the best!

The bext explanation I've found, thank you!

Sir thank u so much 😊 best explanation period

This the man part of the linear in tha potential formation in my he please farmive

this video is incredible, thank you so much!

Thanks Matthew. In a longitudinal design, let's say 5 Time Points, 20 subjects what would be the optimal way to set up the random effects? I feel like whenever I include the intercept or any interaction with tie TIME POINT factor it explains almost all variance in the dependent variable (as it changes from time point to time point, but I want to study the effects of the independent variables changing over time on the dependent variable). Should I just ignore the TIME POINT (or "visit "1, 2, 3 4, 5) factor, as it's implicitly related to the values both in the dependent and independent variables? And just include the "SUBJECT" as a repeated measures account?

Great video

Excellent work

nice explanation. Thank you for posting. Can you share some materials on GLMM please? Thank you so much it really helps.

Great video. I have a question, what would make more sense to be used for accounting inherent agricultural field variability (having spatially separated block on a larger field)? A fixed or random effect?

Hello and thank you for the video I would like to use GLMM multinomial logistic regression mixed model for repeated data with R software, response ~ trt + period + seqTrt + (1|id) do you know a package or a function for this model thank you in advance

how is partial pooling or shrinkage model different then running a fixed effect model on that subset of observations?

Can someone help me to do the plot where we visualize the lines with different intercept and slope? I'm using Rstudio

can I find somewhere examples of random coefficient models where the variable of the random coefficient is not continuous but categorical? ideally written with STATA or SPSS?

SUPER nice!! one question, i have a LMM with df what do they mean?

Country X has 30 states with repeated observation measures of X across 15 years for each state. Is Mixed Effects appropriate to model Y from X with states as random effects?

if I have the more than 3 datasets with different x and y axis then how statistically it can be compared??

Thanks! Great explanation and summary. I wanted to ask if there's a source (paper, book, books) you could point to for this topic? Thanks again

Great

The R code for all this stuff would be great

How can I write a comment on mixed linear model plz

Are LMM and LMEM the same thing?

Awsome explanation. But wait, I just can't get p-values? How do I know which fixed effects are relevant?

how to add the fixed effect: shape, in the formula for nested random effect please?

where is gray and green?? Am I color blind

can we make a collab video?

it was great up until like 16:12 when suddenly randoms graphs from god knows where

6:45

Terrible explanation, just making a simple concept become ultra complex.