[FieldTrip] Permutation test design matrix for grand-averaged ERPs
e.spaak at donders.ru.nl
Wed Oct 24 09:39:39 CEST 2018
It sounds like what you're dealing with is a single group of
participants, each of whom is measured under two conditions. You are
correct in stating that depsamplesT is the way to go here (as Marisha
also suggested). This is, I believe, exactly the case described in
this section of the "gentle" stats tutorial:
and this section of the more "in depth" tutorial:
See those tutorials (and Marisha's email) for more info on how to set
up your design matrix in this case.
On Tue, 23 Oct 2018 at 17:21, Kate Stone <katemsto at gmail.com> wrote:
> Hi again,
> Further to the below, what I would actually prefer to do is use the depsamplesT test on my data structure containing averages over trials for each subject (i.e. the output of ft_timelockanalysis).
> But every time I try, I get the message "length of the design matrix (2) doesn't equal the number of observations (694)" - no matter what the dimensions of the design matrix are. I've set up the matrix exactly as specified in the tutorial (relevant to my data of course). Any idea what this error is about?
> Thanks again,
> On Tue, Oct 23, 2018 at 2:04 PM Kate Stone <katemsto at gmail.com> wrote:
>> Hi all,
>> I've been following the tutorial here to do a time-locked perm test on ERP data: www.fieldtriptoolbox.org/tutorial/cluster_permutation_timelock
>> I have computed grand averages for my two conditions and am using indepsamplesT with ft_timelockstatistics(cfg, GA_1, GA_2).
>> But what should the design matrix be? The example in the tutorial doesn't apply as I am using grand averages and the reference for ft_timelockstatistics doesn't mention the design matrix, although it is definitely required. I have tried design = [1,2], but this doesn't seem to give sensible results.
>> Thanks in advance,
> Kate Stone
> PhD candidate
> Vasishth Lab | Department of Linguistics
> Potsdam University, 14467 Potsdam, Germany
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