The equivalent in Matlab would be:
data = [...
1 1 2 4;
1 3 2 6;
1 6 0 7]
x = (1:10).';
f = @(t) data(t,4)./(data(t,1) + data(t,2) + data(t,3) + x )
y = [ x f(1) x f(2) x f(3) ]
or even simpler:
N = 10;
f = @(t) [(1:N).' data(t,4)./(data(t,1) + data(t,2) + data(t,3) + (1:N).' )]
y = [ f(1) f(2) f(3) ]
the number in f(...) always indicates which row, respectively which y e.g. y1, y2, etc. you are calculating for each column of the output. The brackets [...] are concatenating the result.
Be aware that you need to use the element-wise division operator ./
Generalized for an n x m sized input array, but assuming that the n-column is always the last one of your input Matrix:
N = 10;
f = @(t) [(1:N).' data(t,end)./(sum( data(t,(1:end-1))) + (1:N).' )]
y = cell2mat(arrayfun(f, 1:size(data,1),'uni',0))
But in this case you should think about, if a more vectorized approach like Divakar's answer might be more appropriate.
result:
y =
1 0.8 1 0.85714 1 0.875
2 0.66667 2 0.75 2 0.77778
3 0.57143 3 0.66667 3 0.7
4 0.5 4 0.6 4 0.63636
5 0.44444 5 0.54545 5 0.58333
6 0.4 6 0.5 6 0.53846
7 0.36364 7 0.46154 7 0.5
8 0.33333 8 0.42857 8 0.46667
9 0.30769 9 0.4 9 0.4375
10 0.28571 10 0.375 10 0.41176
din your code?