Hello, Pystan champions,
I have fitted a structural time series model in Stan and used arviz to plot posterior predictive distribution.
here is my code:
import arviz as az idata = az.from_pystan(posterior=fit_TOUR_model, posterior_predictive=[“y_predict”], observed_data=[“y”]) az.plot_ppc(idata, num_pp_samples=2000,data_pairs={“y”: “y_predict”},mean=False)
But I would like to see the posterior plot with time in the horizontal axis and the actual and predictive draws in the vertical axis. Is there any way this can be done using arviz?
Thanks Champs,
Antony
Our GSOC student has started working on regression plots, but currently, these are not yet merged into the codebase.
arviz-devs:main ← utkarsh-maheshwari:lin-reg
opened 03:25PM - 15 Jun 21 UTC
## Description
Here is my initial prototype for linear regression plot. I am ch… ecking if input data is correct in the the user API function. Backend function is quite small, only does the plotting part.
Example Notebook : https://gist.github.com/utkarsh-maheshwari/8d4cd2fd84c763bf85291c3f0881d588
High level design:
We accept 5 args
1. y_model : (Necessary) linear relationship in the form of `y = m1*x1 + m2*x2 + c`
2. idata : (Necessary if any of other args are string) should contain all the necessary variable names in any of the groups.
3. x_data : (Necessary because we cannot identify the x variable name from y_model) seq/str
4. y_data : (Necessary if y_model is Sequence. We can identify the name from y_model) seq/str
5. y_ppc : (Necessary is y_model is sequence. Assumed to be same as y_data if absent) seq/str
Function call:
`plot_lm(y_model = "kid_score ~ Intercept + slope * mom_iq", idata = idata, x_data = "mom_iq")
`
Output :
Shows uncertainty in y_data in orange, uncertainty in y_model in black, OLS regression line in yellow.
Should regression lines be plotted instead of filling the area?
-----------------------------------------------------------------------------------------------------
If y_model is sequence, we're good with plotting it.
If y_model is string like `y ~ slope * x_name + Intercept`, it should be converted to sequence by
- correctly identifying the variable names present in the string.
- look for variable names in inferenceData groups.
- Compute y_model term by term to form desired sequence.
To begin with, we can do it like :
```
rhs, lhs = y_model.split('~')
terms = rhs.split('+') : gives terms of the expression.
var_name = terms.split('*') : gives variable names to be searched in data
```
Iterate over terms
calculate value of each term
add it to the y_model_values
It works well with parsing GLM because I am keeping track of functions in function stack.
Any better way to parse `y_model` to extract the variable names from the string?
Love to know suggestions/reccomendations.
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Thank you, I will keep an eye on the updates.
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