[Rd] (PR#3427)

[Peter Dalgaard BSA]

Dursun.Bulutoglu at afit.edu writes:

> Hi;
> 
>             I am having problems inverting matrices using the function
> solve()
> 
>             For example R can not invert the following matrix
> 
>                  =20
> 
>               [,1]           [,2]              [,3]              [,4]
> [,5]
> 
>   [1,]      25            500              11250           275000
> 7.106250e+06
> 
>   [2,]      500          11250           275000         7106250
> 1.906250e+08
> 
>   [3,]      11250       275000         7106250       190625000
> 5.247656e+09
> 
>   [4,]      275000     7106250       190625000     5247656250
> 1.471719e+11
> 
>   [5,]      7106250   190625000    5247656250   147171875000
> 4.184754e+12
> 
> solve(t(xxmodel)%*%(xxmodel))
> 
> Yields the following massage:
> 
> Error in solve.default(t(xxmodel) %*% (xxmodel)) : singular matrix `a'
> in solve
> 
> The above 5X5 matrix is invertible. It has non-zero eigenvalues. Could
> someone explain whether there is a problem in R's solve() function.

Please use the r-help list unless you are sure that you have found a
bug (and check up on the definition of a bug in the FAQ, and also
details required when reporting).

You seem to be running into a generic problem with matrix inverters,
namely that they are unhappy when the columns are on widely different
scales. Your diagonal elements vary by a factor of about 2e11. Since
detection of linear dependency has to operate with a small tolerance
to account for roundoff, this can become indistinguishable from a
nonsingular matrix with a large range of eigenvalues.

The typical workaround would to rescale the columns of xxmodel.

-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907