On 01/05/2013 10:32 PM, mary wrote:
Thanks for the help:your reply has focused my problem
in particular I'm trying to do a converge process;
the robustm() function is:
function (x,z)
1# {eigen<-eigen(x)
2# d<-madmatrix(z)
##I have created this function to calculate "mad" of a whole matrix
3# eigenc<-eigen$vectors
4# q<-d%*%eigenc
5# invQ<-matrix.inverse(q)
6# sZ<-mdefpos(z,invQ) ##this function
serves me to define positive my new matrix Z, it's X%*%invQ
7# madZ<-madmatrix(sZ)
8# S_X<-q%*%(madsZ)^2%*%t(q)
return(S_X)
}
reviewing this function I realized that it can not be applied in an
iterative manner in the next step because every time a new matrix z should
be fed back into the process and then calculate a new scatter matrix; in the
code above :
(x) is a scatter matrix, Z is a matrix (n*p) of original data that I have
used to obtain a scatter matrix...
in line 6# I need this new matrix sZ because it will be my new (z) if I
reiterate the process on the new scatter matrix S_X, in fact my function
robustm() ask me the scatter matrix and the data matrix so...
how I can do this...and the reiterate the process so that the matrix
converges??
Hi mary,
There are a few different ways to iterate this. One is recursion, where
the function calls itself until the desired precision is obtained:
robustm<-function(x,z) {
# not a good idea to use the function name for the return value
eigenx<-eigen(x)
d<-madmatrix(z)
eigenc<-eigen$vectors
q<-d%*%eigenc
invQ<-matrix.inverse(q)
sZ<-mdefpos(x,invQ)
madZ<-madmatrix(sZ)
S_X<-q%*%(madsZ)^2%*%t(q)
# insert your test for the difference of matrix elements here
keep_going<-???
# if the test does not succeed (no difference <= 0.001)
if(keep_going) xz<-robustm(S_X,sZ)
else xz<-list(finalx<-S_X,finalz<-sZ)
return(xz)
}
Jim
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