[FieldTrip] Regression test and t-test providing almost identical results
Snijders, T.M. (Tineke)
tineke.snijders at donders.ru.nl
Wed Jul 20 13:16:41 CEST 2016
Hi Baptiste,
Indeed, as Jan points out, t-test and regression are all GLM.
You wonder what happened to the condition (cond 2) that you didn't use in the t-test:
In a regression with 3 conditions, the GLM 'contrasts' would be [-1 0 1] and [1 0 -1], so the 2nd condition is coded as ''0", meaning that without the 2nd condition the regression would also give exactly the same results...
Best,
Tineke
________________________________________
From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of jan at brogger.no [jan at brogger.no]
Sent: Wednesday, July 20, 2016 12:25 PM
To: fieldtrip at science.ru.nl
Subject: Re: [FieldTrip] Regression test and t-test providing almost identical results
That is because a t- test and a regression test are the same. See this link: http://stats.stackexchange.com/questions/59047/how-are-regression-the-t-test-and-the-anova-all-versions-of-the-general-linear
Yours,
Jan Brogger
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fieldtrip mailing list
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From: fieldtrip-bounces at science.ru.nl [fieldtrip-bounces at science.ru.nl] on behalf of Baptiste Gauthier [gauthierb.ens at gmail.com]
Sent: Wednesday, July 20, 2016 11:49 AM
To: fieldtrip at science.ru.nl
Subject: [FieldTrip] Regression test and t-test providing almost identical results
Dear Fieldtrippers,
I recently observed a strange behavior with the results provided with the regression statistics provided by “ft_statfun_depsamplesregrT”:
I did a regression test on 3 conditions: cond1, cond2 and cond3:
Here are the results:
[Images intégrées 1]
Output for group-level regression test:
the call to "ft_timelockgrandaverage" took 36 seconds and required the additional allocation of an estimated 0 MB
reading layout from file /neurospin/meg/meg_tmp/MTT_MEG_Baptiste/From_laptop/fieldtrip-20130901/template/layout/NM306mag.lay
the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB
the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB
using "ft_statistics_montecarlo" for the statistical testing
using "ft_statfun_depsamplesregrT" for the single-sample statistics
constructing randomized design
total number of measurements = 57
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 19 levels
number of repeated measurements in each level is 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
computing a parametric threshold for clustering
computing statistic
estimated time per randomization is 0.26 seconds
computing statistic 1000 from 1000
Warning: adding /neurospin/meg/meg_tmp/MTT_MEG_Baptiste/fieldtrip-20160719/external/spm8 toolbox to your MATLAB path
found 13 positive clusters in observed data
found 9 negative clusters in observed data
computing clusters in randomization
computing clusters in randomization 1000 from 1000
using a cluster-based method for multiple comparison correction
the returned probabilities and the thresholded mask are corrected for multiple comparisons
the call to "ft_timelockstatistics" took 55 seconds and required the additional allocation of an estimated 12 MB
Afterwards, for some other reasons I performed directly a T-test between condition 1 and condition 3. Here is the result:
[Images intégrées 2]
Output for group-level T-test:
the call to "ft_timelockgrandaverage" took 36 seconds and required the additional allocation of an estimated 0 MB
reading layout from file /neurospin/meg/meg_tmp/MTT_MEG_Baptiste/From_laptop/fieldtrip-20130901/template/layout/NM306mag.lay
the call to "ft_prepare_layout" took 0 seconds and required the additional allocation of an estimated 0 MB
the call to "ft_selectdata" took 0 seconds and required the additional allocation of an estimated 0 MB
using "ft_statistics_montecarlo" for the statistical testing
using "ft_statfun_depsamplesT" for the single-sample statistics
constructing randomized design
total number of measurements = 38
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 19 levels
number of repeated measurements in each level is 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
computing a parametric threshold for clustering
computing statistic
estimated time per randomization is 0.04 seconds
computing statistic 1000 from 1000
found 9 positive clusters in observed data
found 13 negative clusters in observed data
computing clusters in randomization
computing clusters in randomization 1000 from 1000
using a cluster-based method for multiple comparison correction
the returned probabilities and the thresholded mask are corrected for multiple comparisons
the call to "ft_timelockstatistics" took 46 seconds and required the additional allocation of an estimated 0 MB
I checked that I actually provided the good data and parameters to the function, but I am puzzled by the resemblance (if you invert the sign) of the results. To doublecheck, I directly compared the stat.stat field from the two tests. Here are the results:
[Images intégrées 3]
So basically, it is the same if you round to the 3rd digits after the floating point. It sounds really weird to me.
Has someone ever noticed something similar when performing regression tests? I there any simple mathematical tricks that could explain that?
Best regards,
Baptiste Gauthier, PhD
--
Baptiste Gauthier
Postdoctoral Research Fellow
INSERM-CEA Cognitive Neuroimaging unit
CEA/SAC/DSV/DRM/Neurospin center
Bât 145, Point Courier 156
F-91191 Gif-sur-Yvette Cedex FRANCE
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