[FieldTrip] freqstatistics ivar and uvar
Irina Simanova
irina.simanova at mpi.nl
Wed Jul 11 17:20:41 CEST 2012
Dear all,
I have a very basic question on freqstatistics. I want to compare the
TFR's in 3 experimental conditions over 12 subjects. I run the
freqstatistics with cfg.method = 'depsamplesF'.
cfg.uvar = 1
cfg.ivar = 2
cfg.design = [1:12 1:12 1:12; ones(1,12) 2*ones(1,12) 3*ones(1,12)]
I am confused with the the following lines that FT prints out:
repeated measurement in variable 1 over 12 levels
number of repeated measurements in each level is 3 3 3 3 3 3 3 3 3 3 3 3
I assume it is correct, since it's exactly the same output as I get
with the tutorial within-subjects statistics example. But why is not
it the other way around? There are 3 levels of the independent
variable, and the number of samples in each level is 12?
Thank you!
Best,
Irina
Here is the code:
cfg = [];
cfg.latency = [0 1.5];
cfg.channel = 'all';
cfg.method = 'montecarlo';
cfg.statistic = 'depsamplesF';
cfg.correctm = 'cluster';
cfg.clusteralpha = 0.05;
cfg.clusterstatistic = 'maxsum';
cfg.tail = 1;
cfg.clustertail = 1;
cfg.alpha = 0.05;
cfg.numrandomization = 100;
cfg.neighbours = neighbours;
cfg.parameter = 'powspctrm';
cfg.design = design;
cfg.uvar = 1;
cfg.ivar = 2;
[stat] = ft_freqstatistics(cfg, TFRgrand_1, TFRgrand_2, TFRgrand_3);
computing statistic over the frequency range [3.962 39.937]
computing statistic over the time range [0.000 1.500]
selection powspctrm along dimension 2
selection powspctrm along dimension 3
selection powspctrm along dimension 4
using "ft_statistics_montecarlo" for the statistical testing
using "statfun_depsamplesF" for the single-sample statistics
constructing randomized design
total number of measurements = 36
total number of variables = 2
number of independent variables = 1
number of unit variables = 1
number of within-cell variables = 0
number of control variables = 0
using a permutation resampling approach
repeated measurement in variable 1 over 12 levels
number of repeated measurements in each level is 3 3 3 3 3 3 3 3 3 3 3 3
computing a parametric threshold for clustering
computing statistic
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