loadtimesamplingdata.m

The BNL toolbox is a set of Matlab functions for defining and estimating the parameters of a Bayesi

function obs_variables=loadtimesamplingdata 


N=32;%number of cases 
M=12;%number of items at each signal
B=9; %number of signals within a day
D=7; %number of days
I=12*7*9;

%obs_variables=cell(1,I);
load 'C:\frank\mijnmatlabfuncties\cleanedversion2\example_models\anorex\baynet.txt';
Y=baynet;
Y_d=Y(:,5:end);
z=find(Y(:,5:end)>-1 & Y(:,5:end)<3);
Y_d(z)=0;
z=find( Y(:,5:end)>2);
Y_d(z)=1;
Y_d=[Y(:,1:4) Y_d]    ;
Y_d=[Y_d(:,1:4) Y_d(:,4+[1 4 6 11 2 3 5 7 8 9 10 12])];

%we veronderstellen dat dagen onafhankelijk zijn, dus pp*dagen =aantal
%proefpersonen 
Y_f=zeros(N,I);
for i=1:N
    a=Y_d((i-1)*63+1:i*63,4+[1 :12])';
    %a=Y_d((i-1)*9+1:i*9,4+[1 :40])';
    Y_f(i,:)=a(:)';
end
obs_variables.datamatrix=Y_f;

%name the variables
names=cell(1,I);
for i=1:M
    for j=1:B
        for k=1:D
            c=(k-1)*M*B+(j-1)*M+i;
            names{c}=eval(['''Y_d',int2str(k),'_s',int2str(j),'_i',int2str(i),''';']);
        end
    end
end
obs_variables.names=names;