Hi Donal,
I'm not familiar enough with the SPM version of permutation testing to know how exactly how the thresholding is performed, but since the p<->t conversion is empirically derived, there is no a-priori way of telling randomise to threshold clusters at an uncorrected p of 0.001, although if you assume normality, the p-to-t conversion could be done with MATLAB etc. The argument is just the t-statistic value to threshold at.
Hope this helps,
Kind Regards,
Matthew
> Hi Matthew,
>
> I’m trying to compare what randomise with -c <thresh> gives me compared to similar permutation implementations in SPM. In SPM, I explicitly provide an uncorrected p-value threshold of 0.001.
>
> If FSL requires a t-stat threshold instead, I need to know what the equivalent t-statistic for a p-value threshold of 0.001 is. This isn’t completely trivial, as it depends on the number of subjects.
>
> It would be more unambiguous if the <thresh> argument was, for instance, a z-statistic value. In that case, I could use <thresh> = 3.1 to match the p = 0.001 requirement.
>
> Is there a reference somewhere for the exact form of the -c <thresh> argument i.e. is it a t-statistic or a z-statistic? I’ve looked high and low and can’t find any mention of it!
>
> Thanks for your help,
> Donal
>
>
>> On 29 Mar 2016, at 16:20, Matthew Webster <[log in to unmask]> wrote:
>>
>> Hi Donal,
>> the threshold is the t-statistic value you want to threshold at. Thresholding at a given uncorrected-p would require modifying randomise to run twice, using the voxelwise t-statisic null-distributions from the first run to apply p-thresholds for the second run.
>>
>> Kind Regards
>> Matthew
>>
>>> Dear experts,
>>>
>>> I am trying to use the -c option within randomise to perform cluster-based thresholding. I am wondering what the input argument <thresh> should be in this instance?
>>>
>>> I would like to threshold my statistic images at an uncorrected p-value of 0.001. Can I supply 0.001 as the value of <thresh>, or do I need to supply a t-statistic value?
>>>
>>> Kind regards,
>>> Donal
>
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