CFA and SEM with PyMC #698
Conversation
Signed-off-by: Nathaniel <[email protected]>
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Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
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Think this is ready for review @drbenvincent , @cluhmann , @AlexAndorra . Any feedback/pushback welcome. |
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Giving this another nudge! Thanks! |
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FYI: I'm totally overloaded at the moment, so going to struggle with a review. Will see if things ease up a bit next week. |
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No worries. |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:46Z Perhaps add reference to psychometrics in the title to make it more specific, and aid in discoverability. NathanielF commented on 2024-09-25T11:38:08Z Good idea. |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:47Z nitpick: data set should not have a hyphen; data set or dataset, no? NathanielF commented on 2024-09-25T11:38:41Z changed. |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:47Z Maybe hyperlink SEM and CFA to some introductory materials on the topics (even a Wikipedia link would be good) NathanielF commented on 2024-09-25T11:38:54Z Added links to wikipedia. |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:48Z There are lots of UserWarnings in the notebook. Consider filtering them out. |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:49Z The last line of the equation set contains a typo -- should be \psi_n rather than \psi_3 NathanielF commented on 2024-09-25T11:39:29Z Fixed. Thanks, good catch! |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:50Z Line #29. chol, _, _ = pm.LKJCholeskyCov("chol_cov", n=2, eta=2, sd_dist=sd_dist, compute_corr=True)
Why do you set NathanielF commented on 2024-09-25T11:40:37Z It's not used here in this model, but useful to have computed for analysing the correlations between the constructs post model fit. I demonstrate this below for the full measurement model.... |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:50Z Cool plots! NathanielF commented on 2024-09-25T11:45:43Z Thanks! |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:51Z Line #81. draws=10000, Why so many draws? Bad autocorrelation? NathanielF commented on 2024-09-25T11:42:40Z Yes, it seemed to help generally boost the ESS. The model samples pretty fast so it was easy enough to just boost the samples. |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:52Z typo: reflected NathanielF commented on 2024-09-25T11:42:50Z Fixed. |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:53Z typo in first sentence: individual NathanielF commented on 2024-09-25T11:42:59Z Fixed. |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:53Z Typo: each NathanielF commented on 2024-09-25T11:43:11Z Fixed |
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View / edit / reply to this conversation on ReviewNB fonnesbeck commented on 2024-09-25T03:08:54Z What about measures of relative fit among the models? What do the LOO/WAIC values look like, and are they helpful in selecting the appropriate level of complexity? NathanielF commented on 2024-09-25T11:45:26Z Added the global fit comparison for the SEM and full measurement models. The LOO metrics are quite close, but the full measurement model "wins" on this score. I added a note to say we need to consider the value of the trade-off between expressive power of the SEMs and the questions we can answer in the context of simple global fit comparisons. |
Signed-off-by: Nathaniel <[email protected]>
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Good idea. View entire conversation on ReviewNB |
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changed. View entire conversation on ReviewNB |
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Added links to wikipedia. View entire conversation on ReviewNB |
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Fixed. Thanks, good catch! View entire conversation on ReviewNB |
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It's not used here in this model, but useful to have computed for analysing the correlations between the constructs post model fit. I demonstrate this below for the full measurement model.... View entire conversation on ReviewNB |
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Yes, it seemed to help generally boost the ESS. The model samples pretty fast so it was easy enough to just boost the samples. View entire conversation on ReviewNB |
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Fixed. View entire conversation on ReviewNB |
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Fixed. View entire conversation on ReviewNB |
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Fixed View entire conversation on ReviewNB |
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Added the global fit comparison for the SEM and full measurement models. The LOO metrics are quite close, but the full measurement model "wins" on this score. I added a note to say we need to consider the value of the trade-off between expressive power of the SEMs and the questions we can answer in the context of simple global fit comparisons. View entire conversation on ReviewNB |
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Thanks! View entire conversation on ReviewNB |
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Thanks @fonnesbeck I think i've addressed most of the points you raised. But let me know if you think I should add anything else. Note I also re-parameterised the |
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Sweet! |
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Think you'll have to merge that for me @fonnesbeck, i don't have that power. |
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Woo! Thanks @fonnesbeck |
CFA and SEM with PyMC
Related to this issue here I'm adding a PR to demonstrate the functionality of CFA and SEM models using PyMC
#695
Helpful links
📚 Documentation preview 📚: https://pymc-examples--698.org.readthedocs.build/en/698/