I have a (k x n) matrix. I have initially managed to linearly regress (using the lm function) column 1 with each and every other column and extracted only the coefficients.

```
fore.choose <- matrix(0, 1, NCOL(assets))
for(i in seq(1, NCOL(assets), 1))
{

  abc <- lm(assets[,1]~assets[,i])$coefficients
  fore.choose[1,i] <- abc[2:length(abc)]

}

```

The coefficients are placed in the `fore.choose` matrix.

What I now need to do is to linearly regress column 2 with each and every other column, and then column 3 and so on and so forth and extract only the coefficients. 

The output will be a square matrix of OLS univariate coefficients. Kind of similar to a correlation matrix, but it is the beta coefficients I am interested in. 

```
fore.choose <- matrix(0, 1, NCOL(assets))

```

will initially need to become

```
fore.choose <- matrix(0, NCOL(assets), NCOL(assets))

```