Closed kkdey closed 8 years ago
10,000 iterations take a long time. Are there any other ways around this?
On Thu, Sep 22, 2016 at 10:30 AM Kushal K Dey notifications@github.com wrote:
Closed #25 https://github.com/kkdey/CountClust/issues/25.
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The default is 10,000 ....but you can set it to any value you want, see the example above for instance
I understand. How about tolerance? Makes more sense to constraint on the likelihood change that on the number of iterations.
On Thu, Sep 22, 2016 at 10:34 AM Kushal K Dey notifications@github.com wrote:
The default is 10,000 ....but you can set it to any value you want, see the example above for instance
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There is already a tol
function for that, default to tol=0.1
, meaning it stops when the log likelihood change between successive iterations is less than 0.1, you can change that too if you want to stop early
What's the range of tol? Also does this define the percent decrease in likelihood? For example, .1 is 10 percent decrease in likelihood.
On Thu, Sep 22, 2016 at 10:43 AM Kushal K Dey notifications@github.com wrote:
There is already a tol function for that, default to tol=0.1, you can change that too if you want to stop early
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no it is stronger, it is the absolute change, not relative change in loglikelihood, the smaller the tol
, the better it is, but also would take more time to converge.
Okay let's add the definition to the function description. Thanks!
On Thu, Sep 22, 2016 at 10:53 AM Kushal K Dey notifications@github.com wrote:
no it is stronger, it is the absolute change, not relative change in loglikelihood, the smaller the tol, the better it is, but also would take more time to converge.
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If you use the Github version of the package maptpx
then you can choose the tmax as an option under control.
The default is tmax=10000
out <- FitGoM(ex.counts, K=4, tol=100, control=list(tmax=100))