-
Notifications
You must be signed in to change notification settings - Fork 6
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Errors of importing fitting data #1
Comments
Hi Lili, Thanks for reaching out and for your interest in our work. To answer the question about the logits in Figure 3.
Sincerely, |
Hi Chris, Sorry for taking your first name wrong :) and thank you so much for your reply! I really appreciate your time explaining this. Does it mean you used the posterior samples of the baseline and other learning rates directly without doing any transformation to plot them in Figure 3? Another question about model 1 is that it was mentioned in the paper that there were only three parameters in model 1 but in model 1.py, there are four extra parameters lr_c_t, eps_t, Amix_t, Bc_t and I could not find the risk parameter. Thanks for the shared link. I will check it and see if I can figure the error out. Very sorry that I have so many questions and bothering you again. Best, |
Hi Lili, No problem at all for asking questions. I'm happy that you are interested in the models/code. In looking into your question about model_1.py, it looks like the file was misnamed in the version of the repo that you have. My sincerest apologies for that -- when I uploaded the repo to github for sharing, I changed the file names to be more intuitive and accidentally swapped the names for models_1.py and models2thr9_11.py. I adjusted this and re-pushed the repo, so the now current version is correct. I'm running the model on my computer now and it seems to be running without issue. What's your email? I'll send you the environment.yml in case you want to try installing the conda environment that way. It won't upload here for some reason. For fIgure 3a, I'm not doing any transformations. For figure 3b, I add the samples for the components up, using +1 -1 depending on the condition, with the formula: And then inverse-logit transform. There's a few reasons that you might be seeing larger ranges of values than for you model. One might be because we also have separate components (e.g. lr_baseline, etc.) that get multiplied by the factor scores, so if you don't have these, then the basic components might have a larger range. The range is also probably highly depend on the number of participants -- with more participants, the range tends to be smaller for these group level parameters, because there is more evidence for the mean across participants (e.g. for lr_baseline). |
Hi Chris, Thank you so much for your quick reply. It will be great if you can share the environment file. Thanks for updating the repo! "lr_baseline multiplied by the factor score" refers to the following equation, right? I do have sth similar to this in my model. Here A_base_cov corresponds to the weight parameters (beta_g, beta_d, etc) and cov corresponds to the clinical factors (not the same factors as in your paper, they are the clinical scores for the Parkinson subjects in our study instead. mu_pr corresponds to the mu in your equation. These clinical factors are standardized before fitting to the model). I do agree with the second possible reason. I only have 20 participants tested on this model, which is much smaller than that of yours. I'm so happy to confirm with you about this. At least I got a bit more confidence about my implementation:). Have a nice day! Best, |
Hi Crgagne,
This is Lili, a PhD student from Dublin City University. I'm really interested in the paper you published on eLief and am so impressed by the modeling analysis you made.
It was said in the left plot of figure 3 that the group mean learning rates are in logit space. I'm confused because I thought if it's in logit, the values should all be in the range 0 to 1. So I am trying to replicate your plots in figure 3. However, it gave me errors when I was trying to import the pickle files of the fitting results (please see the following error codes in the Figures_3-4_Behavioral_Model_Exp1_Results.ipynb file and the screenshot of the error information). I have followed the tutorial created the conda environment and installed the related packages. Could not figure out what was wrong on my side. Could you please help with this? Or at least could you please explain a bit the meaning of the logit space? I'm doing the modeling using Rstan apart from replicating your results with your pymc3 codes, so wanna make sure the Rstan models are correct by comparing the posterior distribution reported in your paper.
model_name = 'model=11_covariate=Bi3itemCDM_date=2020_4_27_samples=2000_seed=3_exp=1.pkl'
with open('../fitting_behavioral_model/model_fits/'+model_name, "rb" ) as buff:
model_output = pickle.load(buff)
The text was updated successfully, but these errors were encountered: