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
|