[FieldTrip] Regression test and t-test providing almost identical results
Baptiste Gauthier
gauthierb.ens at gmail.com
Wed Jul 20 15:19:40 CEST 2016
Thank you for your quick answers!
This is very clear now. i'm familiar with GLM as I use it in fMRI but I did
not thought my question in term of contrast.
So, to test for a linear ordering between cond3,cond2 and cond1, would it
be reasonable to compute a regression beta for each subject and then
compare it to zero with a group t-test? (as it would use the data of
condition 2).
thanks again and best regards
2016-07-20 13:16 GMT+02:00 Snijders, T.M. (Tineke) <
tineke.snijders at donders.ru.nl>:
> 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
>
> _______________________________________________
> fieldtrip mailing list
>
>
> ------------------------------
> *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:
>
>
> [image: 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:
>
>
> [image: 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:
>
>
> [image: 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
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> https://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
--
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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