clusterrandanalysis on scalar values + problem with topoplotER

[Marco Buiatti]

Thank you Robert, now EVERYTHING WORKS!!!

More in detail:
On 10/19/06, Robert Oostenveld <r.oostenveld at fcdonders.ru.nl> wrote:
> > computing statistic 2 from 8
> > Warning: Divide by zero.
> >> In fieldtrip-20061016\private\statfun_depsamplesT at 106
> >  In statistics_montecarlo at 278
> >  In fieldtrip-20061016\private\statistics_wrapper at 233
> >  In timelockstatistics at 50
> >
> > ... same output up to statistic 8 and then
>
> Hmm, to me this seems to indicate that you have samples at which you
> do not have any variance, i.e. the computation of the t-score is
> mean_difference/pooled_variance, and since variance is zero, you have
> someting divided by zero, probably resulting in "Inf", i.e. infinite
> numbers. Could it be that you have baseline corrected data that is
> corrected to only one sample (i.e. the sample at time t=0), and that
> hence one sample in the data is always zero?

You were right, and the reason for this was even more trivial: I had
included the reference channel, which is always zero by definition.
Now data are in average reference.

>
> I don't know how a t-score of Inf will affect the other computations.
> But the actual problem is
>
> > ??? Attempted to access (2); index out of bounds because numel()=1.
> >
> > Error in ==> fieldtrip-20061016\private\clusterstat at 65
> >    postailcritval = cfg.clustercritval(2);
> >
> > Error in ==> statistics_montecarlo at 305
> >  stat = clusterstat(cfg, statrand, statobs);
> >
> > Error in ==> fieldtrip-20061016\private\statistics_wrapper at 233
> >  [stat, cfg] = statmethod(cfg, dat, cfg.design);
> >
> > Error in ==> timelockstatistics at 50
> > [stat] = statistics_wrapper(cfg, varargin{:});
>
> That problem is NOT due to the warning above (i.e. the potential t-
> value of inf), but just a small programming error. The sequence of if-
> statements in that section of code was the wrong way around. Please
> find a corrected version of clusterstat.m attached to this mail.
>
> Actually, looking further into the code, I am surprised that you get
> this error. It means that the low level statfun_depsamplesT is using
> a cfg.tail=1 (that corresponds to cfg.clustertail in
> statistics_montecarlo itself). But from your cfg I do not see that
> you explicitely define a positive-tailed test, and the default set at
> the level of statistics_montecarlo should be two-tailed. Do you have
> any ideas about this yourself?
Everything works now with the new clusterstat.m! Here you are my cfg
at the point where it crashed before:
cfg =

               design: [2x6 double]
                 ivar: 1
                 uvar: 2
            statistic: 'depsamplesT'
     numrandomization: 'all'
                alpha: 0.0500
    clusterstatistics: 'maxsum'
     clusterthreshold: 'parametric'
         clusteralpha: 0.0500
        neighbourdist: 0.8000
           neighbours: {1x129 cell}
              latency: [0 0.5000]
               method: 'montecarlo'
             correctm: 'cluster'
              channel: {129x1 cell}
            frequency: []
          avgoverchan: 'no'
          avgovertime: 'no'
          avgoverfreq: 'no'
           channelcmb: []
            precision: 'double'
              version: [1x1 struct]
             previous: {1x6 cell}
                  dim: [129 1 126]
                 tail: 0
             feedback: 'text'
                 cvar: []
                 wvar: []
     clusterstatistic: 'maxsum'
       clustercritval: 2.9200
          clustertail: 0
               factor: 1
           unitfactor: 2
         orderedstats: 'no'
         multivariate: 'no'
            minnbchan: 0
As you see, cfg.tail=0 but I have only one value of
cfg.clustercritval (while the former clusterstat required two).

I checked that results are totally compatible with the ones I obtained
with clusterrandanalysis, and I easily transformed the spatial data I
have (what I erroneously called scalar in my initial e-mail in this
thread) to perform cluster randomization analysis.

Thank you again for the follow up.

Marco


--
Marco Buiatti - Post Doc

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