Mixed Effects Models: A Conceptual Overview Using R

Поділитися
Вставка
  • Опубліковано 6 лют 2025
  • This is a bit of a seminar on mixed effects/ hierarchical / clustered models. I go over the difference between fixed effects vs random effects and how they work together in a single model. I cover variance components only models, random slopes and random intercepts.
    00:00 a bit of background
    07:57 the R stuff

КОМЕНТАРІ •

  • @janh7633
    @janh7633 Рік тому +1

    Nice demonstration of testing competing models and of adding random intercepts and random intercepts & slopes

  • @olearydj
    @olearydj 8 місяців тому +2

    This is one of, if not the best video on the topic here on youtube. Very well done.

  • @ifeanyionyekachi9800
    @ifeanyionyekachi9800 10 місяців тому +1

    this is the best video i have ever came across that simplified the link between longitudinal data and mixed effect models. It is really helpful and thanks a lot for this video

  • @deniecewilliams1981
    @deniecewilliams1981 Рік тому +1

    This was very well presented and the explanations were easy to follow. Thank you. 😊

  • @jelenachuklina271
    @jelenachuklina271 5 місяців тому

    absolutely fantastic overview of LMMs! Good balance of demonstrating many aspects of the model, tools, and of course, interpretation

  • @charleydublin7304
    @charleydublin7304 8 місяців тому

    Excellent presentation, thank you

  • @Sam-tg4ii
    @Sam-tg4ii 6 місяців тому

    Very clear. Thank you.

  • @tavaroevanis8744
    @tavaroevanis8744 Місяць тому

    30:12 I'm getting a "singualr fit" for this model. Did I miss a step?

    • @PsychEDD
      @PsychEDD  28 днів тому +1

      A likely cause is that your random effect explains close to zero variance

    • @tavaroevanis8744
      @tavaroevanis8744 28 днів тому

      @@PsychEDD Thank you for the reply! I ran rePCA(model_5) and discovered that the "subjects" random effect was the source of the singularity. I simplified the model and the singularity warning went away: lmer(pitch ~ context + gender + (1|subject) + (1+context|sentence), data = politeness, REML = FALSE).

  • @Sam-tg4ii
    @Sam-tg4ii 6 місяців тому

    6:05 But previously you said 5-6 groups is ideal for a variable used as random effect. But if you also say participants are used as random effect, isn't usually 100s of participants in a study? Doesn't that mean hundreds of groups in the random effect variable (participant ID)?

  • @jelenachuklina271
    @jelenachuklina271 5 місяців тому

    I wonder, what is the demo tool you use, where you are able so elegantly to edit while doing the demo? :)

  • @brianfisher6799
    @brianfisher6799 10 місяців тому

    I know this is slightly off topic, but maybe time of day when the sample was taken would account for a portion of that final unexplained variance.

    • @PsychEDD
      @PsychEDD  10 місяців тому

      Quite possible :)

  • @Yinjiao_Li
    @Yinjiao_Li 2 місяці тому

    i like the voice. handsome one, hahaha