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This script loads existing text files holding data and runs an anova of N size on them.
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%Runs ANOVAs on the output from featquery. Takes shellscript output.
%Need to put the file/ROI name in filea.
#Name of output file from featquery
%filea=’1a_vlpfc_L’;
%filea=’1b_vlpfc_R’;
%filea=’2a_sma_L’;
%filea=’2b_sma_R’;
%filea=’3a_par_L’;
%filea=’3b_par_R’;
clear
#Group 1 is A, 2 is B.
fileA = [filea ‘.1.txt’];
fileB = [filea ‘.2.txt’];
#Now name all the files that you will import…
fileA2 = [‘cope6/’ fileA];
#… then load and name them.
group1_UR=load (fileA2);
fileA3 = [‘cope7/’ fileA];
group1_US=load (fileA3);
fileA4 = [‘cope8/’ fileA];
group1_BR=load (fileA4);
fileA5 = [‘cope9/’ fileA];
group1_BS=load (fileA5);
fileB2 = [‘cope6/’ fileB];
group2_UR=load (fileB2);
fileB3 = [‘cope7/’ fileB];
group2_US=load (fileB3);
fileB4 = [‘cope8/’ fileB];
group2_BR=load (fileB4);
fileB5 = [‘cope9/’ fileB];
group2_BS=load (fileB5);
#Set up vectors telling matlab which output each of the data points is
%Factor 1: group.
C_M ={ ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘C’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’; ‘M’ };
%Factor 2: Rule type (uni/bivariate)
U_B ={ ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘U’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’; ‘B’ };
%Factor 3: Switch or repetition.
R_S ={ ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘R’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’; ‘S’ };
Y =[ group1_UR; group1_US; group1_BR; group1_BS; group2_UR; group2_US; group2_BR; group2_BS ];
#Run the anova.
p = anovan(Y,{C_M U_B R_S},’model’,’full’)
npsy 