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Thank you very much for providing such a great toolbox! In the case of propensity score matching, how can I infer the members of the control group that were used to compute the treatment effect parame…
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As the method is currently implemented, the 'generate_pseudo_pop()' function combines the tasks of estimating the generalized propensity score (GPS) and then creating matches and testing covariate bal…
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TBA
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Can I just check this is intended behavior? I was expecting the `weights` value to include / apply the sampling weights as those are printed in the summary method. Example to illustrate what I mean:
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I used the example dataset and applied full matching.
Below is the output for 5 matched clusters.
The weights look strange. They don't fit any formula I can think of. The document did not explain …
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The initial version of the package should have:
## Version 0.1.0
1. Mass imputation -- families: `gaussian`, `binomial`, `Gamma`, `inverse.gaussian`, `poisson`, `quasi*` and `MASS::negative.bino…
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I examined the weights from match.data() after applying matchit cem.
The control weights do not sum to 1 and some are even > 1.
```
treat weights subclass
35 0 2.2187500 1
8…
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The following code produces a p-value = 2.0.
I would expect that p values are
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As cox regression analysis needs to test the proportional hazards assumption before modeling, the `tt function` should be used to process the corresponding covariable when the proportional hazards ass…
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Hi there!
I am currently using PsmPy on a rather large dataset and I got the following error in the psm.logistic_ps command:
![image](https://user-images.githubusercontent.com/112629870/20566855…