I would like to reduce the time of imputation as my dataset is more than n=100,000 and it takes very very long (around 20 minutes) to do first iteration.
I don't know what this MCMC estimation settings, so so far I have been using default setting.
Is there a good way to reduce imputation time by changing setting?
Please allow me further question.
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Re: Please allow me further question.
Unfortunately Realcom does not have any estimation settings that would speed up for time taken for a single iteration.
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Re: Please allow me further question.
To reduce MCMC estimation time when imputing large data, you can try: reducing the number of iterations, increasing the interval between saves (thinning), reducing the number of imputed datasets, and if possible, enabling the parallel logic. This will help you improve speed while maintaining data quality. 

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Re: Please allow me further question.
There are no specific estimation settings that can significantly speed up the time required for a single iteration when using MCMC in the imputation process. Adjustments to parameters like the number of chains or iterations won't drastically reduce the processing time needed for a complete iteration. While alternative approaches such as optimizing the dataset or exploring faster algorithms may help, changing the basic settings within the current framework is not enough to accelerate the process meaningfully.
DonaldHardy

DonaldHardy
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- Posts: 2
- Joined: Thu Oct 03, 2024 9:51 am
Re: Please allow me further question.
There are no specific estimation settings that can significantly speed up the time required for a single iteration when using MCMC in the imputation process. Adjustments to parameters like the number of chains or iterations won't drastically reduce the processing time needed for a complete iteration. While alternative approaches such as optimizing the dataset or exploring faster algorithms may help, changing the basic settings within the current framework is not enough to accelerate the process meaningfully.
DonaldHardy

DonaldHardy