Weiwei Shi wrote:
I think the problem might be caused two variables are very correlated.
Should I check the cov matrix and try to delete some?
But i am just not quite sure of your reply. Could you detail it with some
steps?
thanks,
Why not do principal component analysis? To identify the
PCA definately is worth of trying, which was my second thought. But
thanks for the help and also on the suggestion.
On 8/10/05, Kjetil Brinchmann Halvorsen [EMAIL PROTECTED] wrote:
Weiwei Shi wrote:
I think the problem might be caused two variables are very correlated.
Should I check the cov
Hi,
I have a dataset which has around 138 variables and 30,000 cases. I am
trying to calculate a mahalanobis distance matrix for them and my
procedure is like this:
Suppose my data is stored in mymatrix
S-cov(mymatrix) # this is fine
D-sapply(1:nrow(mymatrix), function(i) mahalanobis(mymatrix,
Once I had a situation where the reason was that the variables were
scaled to extremely different magnitudes. 1e-25 is a *very* small number
but still there is some probability that it may help to look up standard
deviations and to multiply the
variable with the smallest st.dev. with 1e20 or
I think the problem might be caused two variables are very correlated.
Should I check the cov matrix and try to delete some?
But i am just not quite sure of your reply. Could you detail it with some steps?
thanks,
weiwei
On 8/8/05, Christian Hennig [EMAIL PROTECTED] wrote:
Once I had a
