Hello Freesurfer experts,
I am getting some unusual results with the lme pipeline, and I would appreciate help with identifying the most likely step in which the problem may have originated.
Running lme_fit_FS.m on a single vertex, followed by lme_F on the output of that (stats) yields a p-value of 0. This is because the fcdf function in matlab rounds to 1 if the F-value is big enough (in our case it rounds at about 80, and our F-value is 123).
This being the case, we are trying to determine whether this is (1) a problem with the data, (2) a problem with the script somewhere, or (3) a system setting I don't know about.
I'm not sure if you have enough information, but can you tell me if the F-values and Beta values look possible (reasonable?)?
Thanks for any advice you can provide. -E
Starting Fisher scoring iterations Likelihood at FS iteration 1 : 484.5267 Gradient norm: 53.7987 Likelihood at FS iteration 2 : 484.5984 Gradient norm: 1.3858 Likelihood at FS iteration 3 : 484.5985 Gradient norm: 0.022222 Likelihood at FS iteration 4 : 484.5985 Gradient norm: 0.00056884 Total elapsed time is 0.24038 seconds
stats =
Bhat: [7x1 double] CovBhat: [7x7 double] bihat: [1x93 double] Covbihat: [93x1 double] phisqhat: 0.0110 SIGMA: [345x5 double] W: [345x5 double] Dhat: 0.0213 X: [345x7 double] Zcols: 1 re: [345x1 double] ni: [93x1 double] lreml: 484.5985
st =
1
K>> stats.Bhat
ans =
3.1531 -0.0559 0.0148 -0.0154 -0.0309 -0.0767 -0.0229
K>> -0.0559 K>> fstats = lme_F(stats,contrastmatrix)
fstats =
F: 123.4649 pval: 0 sgn: -1 df: [1 284.0811]
Hi Eric I don’t see any fundamental problem with this analysis. It looks like a regular lme computation. The p-value is essentially zero (to the extent of numeric precision). Even under the null hypothesis p-values distribute uniform(0,1). When signal is present in the data the distribution of p-values is skewed toward zero. Best -Jorge
El Jueves 20 de marzo de 2014 23:31, Eric Cunningham etc42@hawaii.edu escribió:
Hello Freesurfer experts,
I am getting some
unusual results with the lme pipeline, and I would appreciate help with identifying the most likely step in which the problem may have originated.
Running lme_fit_FS.m on a single vertex, followed by
lme_F on the output of that (stats) yields a p-value of 0. This is because the fcdf function in matlab rounds to 1 if the F-value is big enough (in our case it rounds at about 80, and our F-value is 123).
This being the case, we are trying to determine whether this is (1) a problem with the data, (2) a problem with the script somewhere, or (3) a system setting I don't know about.
I'm not sure
if you have enough information, but can you tell me if the F-values and Beta values look possible (reasonable?)?
Thanks for any advice you can provide.
-E
Starting Fisher scoring iterations Likelihood at FS iteration 1 : 484.5267 Gradient norm: 53.7987 Likelihood at FS iteration 2 : 484.5984 Gradient norm: 1.3858 Likelihood at FS iteration 3 : 484.5985 Gradient norm: 0.022222 Likelihood at FS iteration 4 : 484.5985 Gradient norm: 0.00056884 Total elapsed time is 0.24038 seconds
stats =
Bhat: [7x1 double] CovBhat: [7x7 double] bihat: [1x93 double] Covbihat: [93x1 double] phisqhat: 0.0110 SIGMA: [345x5 double] W: [345x5 double] Dhat: 0.0213 X: [345x7 double] Zcols: 1 re: [345x1 double] ni: [93x1 double] lreml: 484.5985
st =
1
K>> stats.Bhat
ans =
3.1531 -0.0559 0.0148 -0.0154 -0.0309 -0.0767 -0.0229
K>> -0.0559 K>> fstats = lme_F(stats,contrastmatrix)
fstats =
F: 123.4649 pval: 0 sgn: -1 df: [1 284.0811] _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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