A small correction, the correct spm function should be:
spm_invFcdf not spm_Fcdf
On Wed, May 30, 2012 at 11:27 PM, MCLAREN, Donald
<[log in to unmask]>wrote:
> This bug is related to not having the statistics toolbox for MATLAB. We
> are working on removing these dependencies.
>
> In the interim, there are two options:
> (1) Find icdf.m on the internet (google has a few links);
> (2) on line 407 replace
>
> UF = icdf('F',1-.001,df1,df2);
> with
> UF=spm_Fcdf(1-.001,df1,df2);
>
> (3) on line 349-354 replace
>
> if isfield(I,'Thresh') && ~isempty(I.Thresh)
> thresh = icdf('F',I.Thresh,df1,size(xx,1)-df1-1);
> else
> thresh = icdf('F',.5,df1,size(xx,1)-df1-1);
> %disp(thresh);
> end
>
> with
>
> if isfield(I,'Thresh') && ~isempty(I.Thresh)
> thresh = spm_Fcdf(I.Thresh,df1,size(xx,1)-df1-1);
> else
> thresh = spm_Fcdf(.5,df1,size(xx,1)-df1-1);
> %disp(thresh);
> end
>
> Let me know if you have more issues.
>
> Best Regards, Donald McLaren
> =================
> D.G. McLaren, Ph.D.
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Research Fellow, Department of Neurology, Massachusetts General Hospital
> and
> Harvard Medical School
> Website: http://www.martinos.org/~mclaren
> Office: (773) 406-2464
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>
> On Wed, May 30, 2012 at 4:22 AM, zeynep basgoze <[log in to unmask]>wrote:
>
>> Hi everyone,
>>
>> When I try to run GLM Flex I face with this error:
>>
>> Processing Set #48??? Undefined function or method 'icdf' for input
>> arguments of type 'char'.
>>
>> Error in ==> GLM_Flex at 407
>> UF = icdf('F',1-.001,df1,df2);
>>
>> Error in ==> GrupAnalizDeneme at 25
>> I = GLM_Flex(I);
>>
>> I tried almost everything to solve this problem, including renaming
>> the files, rewriting codes, reloading & adding paths etc. However I
>> could not find the solution. Now I doubt that the code I wrote for my
>> experiment could be incorrect, that's why I'm sending it to you...If
>> anyone can help me with that I'd be very appreciated. Thank you so
>> much in advance...
>>
>> Best wishes,
>>
>> Zeynep Basgoze
>>
>>
>> My design is a 2x2x2 mixed ANOVA with 2 within factors: congruency
>> (congruent/incongruent) & valence(positive/negative) with two levels
>> each) and 1 between factor with levels:control&depression
>>
>> the GLM Flex code:
>>
>> clear IN; clear F; clear I;
>> IN.N_subs = [6 6];
>> IN.Between = [1 2]; % The 1 here is so we can run a one sample t-test
>> across all the data
>> IN.BetweenLabs = {{'All'} {'Depression', 'Control'}};
>> IN.Within = [2 2];
>> IN.WithinLabs = {{'Cong' 'Incong'} {'Pos' 'Neg'}};
>> IN.FactorLabs = {'All' 'Group' 'Congruency' 'Valence'};
>> IN.Interactions = {[2 3] [2 4] [3 4] [2 3 4]};
>> IN.EqualVar = [1 0 0 0];
>> IN.Independent = [1 1 0 0];
>>
>> F = CreateDesign(IN);
>>
>> figure(1); imagesc(F.XX); shg
>>
>> I.OutputDir = pwd;
>> I.F = F;
>> I.Scans = {'Hasta_Emine_con9.nii' 'Hasta_Emine_con10.nii'
>> 'Hasta_Emine_con11.nii' 'Hasta_Emine_con12.nii' 'Hasta_MAT_con9.nii'
>> 'Hasta_MAT_con10.nii' 'Hasta_MAT_con11.nii' 'Hasta_MAT_con12.nii'
>> 'Hasta_Nuran_con9.nii' 'Hasta_Nuran_con10.nii' 'Hasta_Nuran_con11.nii'
>> 'Hasta_Nuran_con12.nii' 'Hasta_Sanem_con9.nii' 'Hasta_Sanem_con10.nii'
>> 'Hasta_Sanem_con11.nii' 'Hasta_Sanem_con12.nii' 'Hasta_Sati_con9.nii'
>> 'Hasta_Sati_con10.nii' 'Hasta_Sati_con11.nii' 'Hasta_Sati_con12.nii'
>> 'Hasta_Yunus_con9.nii' 'Hasta_Yunus_con10.nii' 'Hasta_Yunus_con11.nii'
>> 'Hasta_Yunus_con12.nii' 'Kontrol_Ali_con9.nii' 'Kontrol_Ali_con10.nii'
>> 'Kontrol_Ali_con11.nii' 'Kontrol_Ali_con12.nii'
>> 'Kontrol_Dilek_con9.nii' 'Kontrol_Dilek_con10.nii'
>> 'Kontrol_Dilek_con11.nii' 'Kontrol_Dilek_con12.nii'
>> 'Kontrol_Emre_con9.nii' 'Kontrol_Emre_con10.nii'
>> 'Kontrol_Emre_con11.nii' 'Kontrol_Emre_con12.nii'
>> 'Kontrol_Nazli_con9.nii' 'Kontrol_Nazli_con10.nii'
>> 'Kontrol_Nazli_con11.nii' 'Kontrol_Nazli_con12.nii'
>> 'Kontrol_Selgin_con9.nii' 'Kontrol_Selgin_con10.nii'
>> 'Kontrol_Selgin_con11.nii' 'Kontrol_Selgin_con12.nii'
>> 'Kontrol_Sibel_con9.nii' 'Kontrol_Sibel_con10.nii'
>> 'Kontrol_Sibel_con11.nii' 'Kontrol_Sibel_con12.nii'};
>> I.RemoveOutliers = 0;
>> I.minN = 2;
>> I.DoOnlyAll = 1;
>> I.CompOpt=0;
>>
>>
>> I = GLM_Flex(I);
>>
>> %%
>> I.Cons(1).name = 'All';
>> I.Cons(1).Groups = {1};
>> I.Cons(1).Levs = 1;
>> I.Cons(1).ET = [];
>> I.Cons(1).mean = 0;
>>
>> I.Cons(2).name = 'Group Effect';
>> I.Cons(2).Groups = {2 3};
>> I.Cons(2).Levs = 2;
>> I.Cons(2).ET = [];
>> I.Cons(2).mean = 0;
>>
>> I.Cons(3).name = 'Congruency Effect';
>> I.Cons(3).Groups = {4 5};
>> I.Cons(3).Levs = 2;
>> I.Cons(3).ET = [];
>> I.Cons(3).mean = 0;
>>
>> I.Cons(4).name = 'Valence Effect';
>> I.Cons(4). Groups = {6 7};
>> I.Cons(4).Levs = 2;
>> I.Cons(4).ET = [];
>> I.Cons(4).mean = 0;
>>
>> I.Cons(5).name = 'Group by Congruency';
>> I.Cons(5). Groups = {8 9 10 11};
>> I.Cons(5).Levs = [2 2];
>> I.Cons(5).ET = [];
>> I.Cons(5).mean = 0;
>>
>> I.Cons(6).name = 'Group by valence';
>> I.Cons(6). Groups = {12 13 14 15};
>> I.Cons(6).Levs = [2 2];
>> I.Cons(6).ET = [];
>> I.Cons(6).mean = 0;
>>
>> I.Cons(7).name = 'Congruency by Valence';
>> I.Cons(7). Groups = {16 17 18 19};
>> I.Cons(7).Levs = [2 2];
>> I.Cons(7).ET = [];
>> I.Cons(7).mean = 0;
>>
>> I.Cons(8).name = 'Group by Congruency by Valence';
>> I.Cons(8). Groups = {20 21 22 23 24 25 26 27};
>> I.Cons(8).Levs = [2 2 2];
>> I.Cons(8).ET = [];
>> I.Cons(8).mean = 0;
>>
>>
>> I = GLM_Flex_Contrasts(I);
>>
>
>
