Categorical covariates in parameter estimation
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Hello,
Do the SimBIology parameter estimation functions support categorical covariates?
Thank you,
Abed
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Joe Myint
2023-11-4
编辑:Joe Myint
2023-11-4
Hello Abed,
Take a look at this example and see if it is what you are looking for. This example shows how to estimate category-specific (such as young versus old, male versus female) parameters using PK profile data from multiple individuals.
Hope it helps, Joe
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Arthur Goldsipe
2023-11-6
Do you want to do nonlinear regression or nonlinear mixed effects (NLME)? The feature Joe mentions in his comment only applies to nonlinear regression. If you want to use categorical covariates with sbiofitmixed (for NLME), then there's no direct support for categorical covariates. I have an idea for a workaround, but I'd need to think about it a little more. If you are interested in the workaround, please let me know and I can investigate it a bit further. It would also be helpful to know more about your covariate model. How many categories are in your categorical covariate? What does your covariate model look like?
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Jeremy Huard
2023-11-23
编辑:Jeremy Huard
2023-11-23
I would recommend to convert categorical covariates into dummy variables or one-hot vectors, where each category is converted into a new binary column.
However, I would only use
categories and all zeros would encode for the last category.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1550577/image.png)
Please note that I will be using a dataset shipped with MATLAB that does not contain time courses. But the categorical variable it contains will help illlustrate the general idea.
load patients
T = table(Age,Height,Weight,Smoker,...
SelfAssessedHealthStatus,Location,...
'RowNames',LastName);
T = convertvars(T,@iscellstr,"string")
In the table above, the variable SelfAssessedHealthStatus contains 4 categories: Poor, Fair, Good, Excellent.
You can generate dummy variables for it with:
Tstatus = convertvars(T(:,"SelfAssessedHealthStatus"), "SelfAssessedHealthStatus","categorical");
Tstatus = onehotencode(Tstatus);
T = [Tstatus, T]
Now, we can use Poor as the reference group, meaning that when all other groups are 0, then the category is Poor:
T.Poor(:) = 0
During fitting, we can now use a covariate model such as:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1550582/image.png)
Here,
(the population estimate) will correspond to the case where SelfAssessedHealthStatus = Poor,
will correspond to SelfAssessedHealthStatus = Fair,
will correspond to SelfAssessedHealthStatus = Good,
will correspond to SelfAssessedHealthStatus = Excellent.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1550587/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1550592/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1550597/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1550602/image.png)
I hope this helps.
Best regards,
Jérémy
更多回答(1 个)
Sulaymon Eshkabilov
2023-11-3
Yes, it should work. See this doc: https://www.mathworks.com/help/simbio/ref/groupeddata.createdoses.html
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