# output of timelockstatistics

Robert Oostenveld r.oostenveld at FCDONDERS.RU.NL
Wed Mar 12 16:07:37 CET 2008

```Hi Marcel,

On 12 Mar 2008, at 14:50, Marcel Bastiaansen wrote:
> I used timelockstatistics (method montecarlo, i.e. cluster
> randomization) to compare the significance between two erp's.
> Here's the output struct.
>
> stats112_113 =
>
>                    prob: [30x600 double]

your input consists of 30 channels and 600 timepoints. For each of
the 30x600 samples a probability is computed. In your case
(montecarlo and correctm=cluster) the probabilities of neighbouring
chan_time points will often be identical, as they belong to the same
cluster.

>             posclusters: [1x5 struct]

There are 5 clusters with a positive summed t-value

>     posclusterslabelmat: [30x600 double]

this contains the cluster number that each sample belongs to, or a 0
if it does not belong to a sampple. Try imagesc
(stat.posclusterslabelmat)

>         posdistribution: [1x1000 double]

This is the randomization distribution for the statistic of interest
for the positive tail, i.e. the sum of the t-values in the largest
cluster in each randomization. Do hist(stat.posdistribution) and look
at the content of stat.poscluster(1) to see where the first cluster
lies in that distribution.

>
>             negclusters: [1x20 struct]
>     negclusterslabelmat: [30x600 double]
>         negdistribution: [1x1000 double]

idem for negative clusters, except that there are 20 in total (note
that not all might be significant).

for each sample contains a 0 if not significant, or a 1 if significant.

>                    stat: [30x600 double]

for each sample contains the t-value (assuming that you used that as
statistic). So you can do imagesc(stat.stat), and then alpha
statsitic for the first positive cluster can be retrieved using sum
(stat.stat(find(stat.posclusterslabelmat(:)==1)). This should match
the content of the stat.posclusters(1) structure.

>                  dimord: 'chan_time'
>                   label: {30x1 cell}
>                    time: [1x600 double]

The other fields have the first dimension "channel" and second
dimension "time". For freqstatistics on TFR data you would get
dimord='chan_freq_time'

>                     cfg: [1x1 struct]

this was the configuration used in the computation, including all
defaults that were set.