Linear mixed effects models - the basics

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  • Опубліковано 23 гру 2024

КОМЕНТАРІ • 65

  • @tinAbraham_Indy
    @tinAbraham_Indy 2 місяці тому +2

    I could not totally understand the mixed-effects model until I watched this video. Thank you very much.

  • @EcologyInsights
    @EcologyInsights Рік тому +4

    This is the best video I’ve seen on this topic.

  • @22ndCatch
    @22ndCatch 11 місяців тому +1

    Watched four videos on this, and this was the one that made it click. Thanks for your relatable breakdown!

  • @Grbec4
    @Grbec4 Рік тому +12

    Great explanation and the visuals take it to the next level. Thank you very much!

  • @fabioramilli8863
    @fabioramilli8863 2 роки тому +7

    Excellent explanation! I wish I had seen this video years ago, I would have saved myself a lot of time to get in to the topic...

  • @MrRangerXdxY15
    @MrRangerXdxY15 2 роки тому +3

    I have yet to see such a good video explaining LMM. Thanks from Zürich!

  • @lors6739
    @lors6739 Рік тому +2

    Thank you very much. This is very helpful for a person who has no prior knowledge to statistic. This will definitely help my research project.

  • @spulwasser
    @spulwasser Місяць тому +1

    Tack Andreas! I'm trying to figure this out to analyze agricultural data, where several plant populations were scored for a disease at different locations. And per location the same 4 individuals are scored as control (sometimes invarious plot positions in the field). My boss is telling me to do LMM and calculate adjusted means, but I don't understand what that...means😂. This was a great intro for dummies like me👍

  • @DanielGarciaGarcia-c5t
    @DanielGarciaGarcia-c5t Рік тому +2

    Spectacularly well explained. Thanks for that.

  • @kendesmarais9018
    @kendesmarais9018 Рік тому +3

    Excellent job explaining this in an understandable way! Thank you so much!

  • @claudiaaguero5338
    @claudiaaguero5338 2 місяці тому +1

    This was SO helpful and easy to follow. Thanks so much!

    • @tilestats
      @tilestats  2 місяці тому

      Glad it was helpful!

  • @nataliastefanikova3238
    @nataliastefanikova3238 Рік тому +3

    I finally understand the topic! Thank you so much,

  • @nafisanwari6288
    @nafisanwari6288 2 роки тому +2

    Superbly explained with great visuals

  • @juditmaymo
    @juditmaymo 8 місяців тому +1

    Incredible video!!

  • @OMARRAFIQUE-oz5td
    @OMARRAFIQUE-oz5td Рік тому

    At 11:26, -6.0, -18.0 and -21.0 are not intercepts. They are slopes of Subjects 2, 3 and 4 with the slope of Subject 1 as the reference. Please correct me if I am wrong.

    • @tilestats
      @tilestats  Рік тому

      All individuals have the same slope because the lines are parallel. -6.0, -18.0 and -21.0 are how much lower the intercepts are for subject 2, 3 and 4 compared to the reference person, which is person 1.

    • @OmarRafique-op7bv
      @OmarRafique-op7bv Рік тому

      @@tilestats Thanks for reply but my question is how can we talk about individual slopes in a simple linear model which is not a mixed effects model. In a LM there is one overall intercept and every independent variable has a slope associated with it but you are associating intercepts with every individual independent variable. Can't get it.

    • @tilestats
      @tilestats  Рік тому +1

      A simple linear regression model like this (as explained in the beginning of the video):
      Weight = intercept + Weeks
      can only have one intercept.
      Using a multiple linear regression model, we can treat the individuals as a factor (because we have repeated measurements of the same subjects):
      Weight = intercept + Weeks + Subjects
      This model has several intercepts. Have a look at my video about multiple linear regression to get the basic idea to include a factor in linear regression:
      ua-cam.com/video/AP_K7SaKkIE/v-deo.html

  • @jonascruz6562
    @jonascruz6562 2 роки тому +2

    Great explanation !!! Thank you

    • @jonascruz6562
      @jonascruz6562 2 роки тому +1

      One more subscriber!! Greetings from Brazil

  • @gangwang1658
    @gangwang1658 Рік тому +1

    Excellent explanation! If we have a linear model lm2=weights ~ weeks + personId, then Sum of Squared Error or Residual Standard Error will be 11.8 which is close to LMM model with random intercepts. And Even more if we use a interaction terms "weeks*PersonID" then SSE is 4.5. So, how do we explain the benefits of LMM for these models?

    • @tilestats
      @tilestats  Рік тому +3

      In addition to the things that I discuss in the video, such as that of assumptions, you need to estimate more parameters in the LM model. If we would have 100 individuals, the LM would estimate at least 100 parameters with associated p-values (which affect the degrees of freedom). Since we are not interested in making inferences on each individual, it makes more sense to use LMM because you then treat the individuals only as a random effect.

  • @awfulguitar
    @awfulguitar 2 роки тому +2

    Fantastic explanation! Thank you :)

  • @jamesbelongini5371
    @jamesbelongini5371 2 роки тому +2

    this is great, thank you.

  • @JinXing-j1l
    @JinXing-j1l Місяць тому +1

    You are GREAT!

  • @tshepisomokoena5075
    @tshepisomokoena5075 9 місяців тому +1

    Great video!

  • @basilio8417
    @basilio8417 Рік тому +2

    Hello Andreas. First, congratulations on your magnificent videos. They are crystal clear and a very good resource. I have made some calculations and it seems that the linear regression model matches the one you showed at the beginning of the video, although the intercept I calculated is 93.0. The rest is the same as you. I don't know if I am missing something. Thank you!

    • @tilestats
      @tilestats  Рік тому

      How did you calculate? Did you use a software?

    • @basilio8417
      @basilio8417 Рік тому

      @@tilestats I calculated it with both SPSS and Medcalc, and the results were the same. I can send you the file if you want. Thank you

    • @basilio8417
      @basilio8417 Рік тому +1

      Oh, no, sorry, I have checked again and there was an error copying the values. The result is fine

  • @HeyImRod
    @HeyImRod Рік тому +1

    Thanks, super clear!

  • @fazlfazl2346
    @fazlfazl2346 Рік тому

    Hi. Great video. Are there any slide or notes for these lectures that are available????

    • @tilestats
      @tilestats  Рік тому

      Check my homepage:
      www.tilestats.com/shop/

  • @will74lsn
    @will74lsn 8 місяців тому

    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?

    • @tilestats
      @tilestats  8 місяців тому

      Have you seen the second video?
      ua-cam.com/video/oI1_SV1Rpfc/v-deo.html

    • @will74lsn
      @will74lsn 8 місяців тому

      @@tilestats thank you for your answer. The example is with weeks as continuous (slope). Was there a random coefficient with a categorical variable that I missed?

  • @kendesmarais9018
    @kendesmarais9018 Рік тому +1

    Can you recommend a text (in english) that addresses the broader subject of mixed effects models in just as an understandable way as your video?

    • @tilestats
      @tilestats  Рік тому

      No, sorry. Internet is my main source nowadays.

  • @raihanalmiski
    @raihanalmiski 2 роки тому

    Thanks for the explanation, if the subject treat as random effect, then what is the fixed effect?

    • @tilestats
      @tilestats  2 роки тому

      Not sure I understand your question.

    • @raihanalmiski
      @raihanalmiski 2 роки тому

      @@tilestats you say in the video, the subject is a random effect, then which variable treat as fixed effect? 🙏

    • @tilestats
      @tilestats  2 роки тому

      In this example, the intercept is random whereas the slope is fixed (because all 4 individuals are assumed to have the same weight loss). Watch the second video, which will give you more examples between random and fixed effects:
      ua-cam.com/video/oI1_SV1Rpfc/v-deo.html

  • @yolandayeung3225
    @yolandayeung3225 2 роки тому

    Great video! What if I have independent samples across 3 times measurement time?

    • @tilestats
      @tilestats  2 роки тому

      Then you simply use linear regression:
      ua-cam.com/video/AP_K7SaKkIE/v-deo.html

  • @yvet598
    @yvet598 2 роки тому

    interesting example! But I still have a question: in this example, reason for causing failure is that individuals have different weights at begin, if we use traditional liner model and just adjust this factor as a covariate, is that ok? and what's the different between the two models?

    • @tilestats
      @tilestats  2 роки тому

      From 7:00 I compare the two methods.

    • @yvet598
      @yvet598 2 роки тому

      Thanks for your answer, sorry, I just misunderstood the meaning in 7:00, I watched it again. And if the LMM is the blue line, is LM the orange line, which means the two methods are different in shape and position? And there is a suppose that random effects should have more than 5 levels, or you can use the fixed effect, is that means in that way LM is equal to LMM (for LM just include fixed effect)?

  • @shipship6479
    @shipship6479 Рік тому

    I really enjoyed your video and I have a few questions. Could you please explain when a linear mixed model can be used in situations where there are missing values, such as when only two time points are measured and some subjects are not measured at one of the time points? Also, I'm curious if the random intercept model(two measurements) still has the same p-value as ANOVA with paired-t when dealing with missing values. Thank you!

  • @javierhernando5063
    @javierhernando5063 2 роки тому

    I can just don't get how you explain the interaction time:group of intervention in a simple clinical trial in a longitudinal study. When is significant time:group of intervention, does it mean that time has an effect on the results? But the patients are under a trial intervention? This means something I guess. How would you explain it?

    • @tilestats
      @tilestats  2 роки тому

      The interaction time:group (for example Group A and B) means that group A and B have different slopes. Have you seen my second video about Linear mixed-effects models? In this video, I show that the ones on diet A lose weight faster compared to the ones on diet B, given that the interaction term is significant.

    • @javierhernando5063
      @javierhernando5063 2 роки тому

      @@tilestats I have seen it now, really good video and it explains the evolution of the 2 diets across time. So, imagine if you just have one group, measuring the effect of diet 1 across time; how would you put it in words that time as a covariate has a significant value?

    • @tilestats
      @tilestats  2 роки тому

      That the slope is significantly different from zero, which means that the diet significantly change the weight over time.

  • @OMARRAFIQUE-oz5td
    @OMARRAFIQUE-oz5td Рік тому

    At 8.21, you say that "multiple leaner regression model does not give an overall intercept". This is confusing as it is actually the expected value of the response variable when all predictors equal zero. Please clarify.

    • @tilestats
      @tilestats  Рік тому +1

      Multiple linear regression gives intercepts for each individual in this example, but the output does not give an overall (mean) intercept for all individuals.

    • @OMARRAFIQUE-oz5td
      @OMARRAFIQUE-oz5td Рік тому

      ​@@tilestats So it is only for this example. When does multiple linear regression give an overall slope? Could you please point me towards a case?

  • @bessidhoummahfoudh3757
    @bessidhoummahfoudh3757 2 роки тому

    Can this be used as a replacement to T-tests when the samples are small (e.g., 16 per group)?

    • @tilestats
      @tilestats  2 роки тому +1

      No, but a t-test works fine for small sampels, as long as you fulfill the assumptions.

    • @bessidhoummahfoudh3757
      @bessidhoummahfoudh3757 2 роки тому

      @@tilestats my samples are small (16 /16) and only random assignment was conducted, so I am violating one assumption (random selection)...what are the best tests for testing the means differencs withing groups and between groups? Thank you!

    • @tilestats
      @tilestats  2 роки тому +1

      If you took a sample of 32 independent subjects and randomly assigned them into two groups, it sounds like an unpaired t-test is appropriate. Have a look at this video:
      ua-cam.com/video/dYJLUvo0Q6g/v-deo.html

    • @bessidhoummahfoudh3757
      @bessidhoummahfoudh3757 2 роки тому

      @@tilestats ok I'll thank you very much for your help!

  • @laxmanbisht2638
    @laxmanbisht2638 2 роки тому

    Are mixed effect models same as random parameter models?

    • @tilestats
      @tilestats  2 роки тому +2

      Yes, it has a lot of names
      en.wikipedia.org/wiki/Multilevel_model

  • @vic7181vic
    @vic7181vic Рік тому +1

    Thank you so much for your excellent explanations. Can you please create a video that explains in simple terms that when we should consider a variable "random" and when "fixed"?
    As some feedback, is it possible to pronounce "d" in the word "moDel"? You pronounce it "moWel". This and other odd pronounciations distract the listener.

  • @chrislloyd5415
    @chrislloyd5415 Рік тому

    There is absolutely nothing wrong with the fixed effects model. Using a dummy variable for each individual captures the dependence within individual that would otherwise be there. In fact, it is a more robust solution since the normal assumption may be incorrect. In the example you give, there is absolutely no reason to make the almost untestable assumption that the intercepts a a draw from a normal distribution. And if we are are interested in the effect of dieting, why would we make an extra untestable assumption. Bottom line is that RE models are ill-advised in most situations. BTW: I am a Professor of Statistics.