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Bayesian VPC #10

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mohamed82008 opened this issue Sep 23, 2022 · 3 comments
Open

Bayesian VPC #10

mohamed82008 opened this issue Sep 23, 2022 · 3 comments

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@mohamed82008
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We can do simulations from the prior or posterior predictive distribution and then do a visual predictive check (VPC) plot.

@cbdavis33
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I have a couple versions of visual predictive checks in R using the Stan/Torsten output. I'm in general not a huge fan of the Upsalla (and now industry standard) VPC plots, so I haven't thought about how to replicate those, but that might be necessary, since that's what people want to see

@storopoli
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We use this following approach:

In Pumas, for models with continuous derived variables, the non-parametric quantile regression approach discussed in Jamsen et al. is used. Discrete and Time to Event models are also supported through the vpc function and can be used without any change in syntax.

So we use a quantile regression.
Is this the "Upsalla" standard?

@mohamed82008
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In Pumas it will be as simple as replacing ... with a few arguments in the following 3 liner:

sims = simobs(...)
vpc_out = vpc(...; simulations = sims)
vpc_plot(vpc_out)

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