Linear mixed effects models

When to choose mixed-effects models, how to determine fixed effects vs. random effects, and nested vs. crossed sampling designs. Requirements and assumptions of mixed-effects models, and how to evaluate them. How mixed-effects models can improve parameter estimation with partial pooling/shrinkage.

[@fiore1394]

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.

[@theonetojump]

By far the best explanation on LMM. Thanks

[@nicolasmellalopez3684]

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

[@zheyuanpei5543]

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

[@muffinsniper]

Studying psychology and this was super helpful!! Thanks

[@kersting1712]

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

[@Maicolacola]

This was incredibly helpful. Thank you!

[@gavinaustin4474]

Thanks Matthew. Very good explanation.

[@sean_gruber]

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

[@amalnasir9940]

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

[@lintonfreund]

this video is incredible, thank you so much!

[@jcmt8178]

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?

[@mallorythomas725]

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

[@gisellechuwen2236]

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

[@srishtigureja6534]

This was really helpful. Saved my day!!

[@stevenash2869]

The bext explanation I've found, thank you!

[@ernstpaulswens]

thanks! very clear visualisations

[@multitaskprueba1]

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

[@y37chung]

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?

[@francycharuto]

Random

[@rohanshinkre]

Sir thank u so much 😊 best explanation period

[@will74lsn]

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?

[@milanfilipovic3648]

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

[@user-mh7px2uy1k]

Excellent work

[@driouechehocine535]

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

[@bezaeshetu5454]

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

[@JinaneJouni]

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

[@marco.miglionico]

Great video

[@chacmool2581]

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?

[@divyaagrawal6740]

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

[@maxgav13]

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

[@madisonlong3182]

Just to update anyone else who comes looking for a citation, the manuscript Naseem linked was recently published in Advances in Methods and Practices in Psychological Science! journals.sagepub.com/doi/10.1177/2515245920960351

[@ufoisback5088]

The R code for all this stuff would be great

[@ericdoe1129]

Great

[@a.s.3874]

Are LMM and LMEM the same thing?

[@ViriatoII]

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

[@MatthewEClapham]

It may depend on the program you're using, but the authors of the lmer function (lme4 package in R) chose not to give p values. However, there are standard errors for each coefficient and you can get the 95% confidence interval on each fixed effect by running the confint() function on the model output.

[@marinadh4402]

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

[@ghadaelkhawaga7081]

How can I write a comment on mixed linear model plz

[@statisticsappliedmathemati810]

can we make a collab video?

[@danparish1344]

Did you get that collab?

[@Breizh1999]

6:45

[@danhelll8768]

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

[@andrewchen7342]

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