Least square optimization in R -


i wondering how 1 solve following problem in r.

we have v vector (of n elements) , b matrix (of dimension m x n). e.g:

    > v      [1] 2 4 3 1 5 7      > b          [,1] [,2] [,3] [,4] [,5] [,6]     [1,]    2    1   5    5    3    4     [2,]    4    5   6    3    2    5     [3,]    3    7   5    1    7    6 

i looking m-long vector u such that

    sum( ( v - ( u %*% b) )^2 ) 

is minimized (i.e. minimizes sum of squares).

you describing linear regression, can done lm function:

coefficients(lm(v~t(b)+0)) #      t(b)1      t(b)2      t(b)3  #  0.2280676 -0.1505233  0.7431653  

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