Using Flexplot for Mixed Models

Рет қаралды 9,156

Жыл бұрын

Video about variance explained: • Explaining Variance Ex...
Here's the dataset I'm using: quantpsych.net/data/jedi.csv
And here's Steven He's channel (hilarious): / stevenhe
Link about EDA versus CDA: • Ethics in Statistics P...
My Multivariate playlist: • Multivariate Statistics
And here's a paper I wrote about my eight step approach to data analysis: psyarxiv.com/r8g7c/
Undergraduate curriculum playlist (GLM-based approach): kzfaq.info?list...
Graduate curriculum playlist (also GLM-based approach): kzfaq.info?list...
Exonerating EDA paper: psyarxiv.com/5vfq6/
Download JASP (and visual modeling module): www.jasp-stat.org

Пікірлер: 34

@maxduvall9055 Жыл бұрын

<a href="#" class="seekto" data-time="86">1:26</a> "BAM! You haven't seen this", he says while covering up the estimates we cannot see... Good one, Dr. Fife, you got us.

@allisonholevoet1328 Жыл бұрын

Wow. Thank you for this. While the rest of the team is out eating dinner, I'm in bed early watching your videos instead of Netflix. :) Keep up the great content.

@QuantPsych 4 ай бұрын

I'm better than netflix!?!?!?! That's high praise :)

@daitoc08 Жыл бұрын

Thanks very much!

@jessewallace-webb4475 Жыл бұрын

Thank you for creating this amazing feature! Is it going to be available in jamovi at some point too?

@TheBjjninja Жыл бұрын

I love flexplot! Nice, Dr. Dustin.

@ryanoleary2928 Жыл бұрын

Thank you for all your videos! I was wondering, could you cover how to do linear contrasts within a mixed model?

@reddotmusic6251 Жыл бұрын

Thanks for these videos, they really help me a lot! I have one question (it's not the main topic of this video but I've not seen a good explanation for it also in other videos): why does it make sense to model sth as a fixed AND random effect ? Would appreciate an answer:). Keep up the great work, big fan!

@hm.91 4 ай бұрын

Best mixed models video everrrr

@QuantPsych 4 ай бұрын

Glad you think so!

@ghaiszriki7912 Жыл бұрын

Hi (: Thaks a million man, I am almost no more scared by mixed models

@Supertajt Жыл бұрын

Thank you so much for your videos! Can you do a video where you explain how you would report an LMM in a table for publication?

@QuantPsych Жыл бұрын

Good idea!

@Rockmonanov 10 ай бұрын

Not sure if this means anything to you: "Error in model$model : $ operator not defined for this S4 class", I just keep getting that anytime I use compare.fits for anything, everything else works though.

@vornamenachname7650 9 ай бұрын

Hi I also had the problem. I think I found the reason: you cannot library/require lmerTest when using flexplot or it will interfere somehow

@Rockmonanov 9 ай бұрын

@@vornamenachname7650 great! I thought it might have something to do with one package interfering with another. Thanks!

@user-vd6gh3ei3y Жыл бұрын

Hi, I'm dong a similar analysis. My model is: model

@QuantPsych Жыл бұрын

Please report on github.com/dustinfife/flexplot

@AlceaRose Жыл бұрын

I have the same problem; did you manage to fix it somehow ?

@brazilfootball Жыл бұрын

Does Flexplot not work well with models built from the glmmTMB package? I get this error: Error in if (n1 < nrow(data) | n2 < nrow(data)) { : argument is of length zero

@QuantPsych Жыл бұрын

Probably not. I wish flexplot automatically worked with all types of models, but unfortunately, there's too much flexibility in how others can store model information. You can add it as a feature request on github: github.com/dustinfife/flexplot

@evanvaldes610 Жыл бұрын

What would you recommend If I am running into convergence issues with a specific model, and I reduce the model to the point where it does converge, but model comparisons show that the more complex albeit convergence issues model is the better explaining/fitting model. How do I go about understanding that inconsistency/is it common? This is the warning: Warning: Model failed to converge with max|grad| = 0.00348609 (tol = 0.002, component 1)

@TheBjjninja Жыл бұрын

I'd probably use some statistical tests on the residuals to make a decision and weigh what the purpose of the model is used for also. If the residuals look better on a simpler model and I'm using the model for inference purpose and the AIC metrics look only very slightly better on a more complicated model, then I'd probably use the simpler model. If you are the using the model to make predictions, then of course use the complicated model.