A quick comment on this: imputation is an option to make things
technically work, but it is not
necessarily good. Imputation always introduces some noise, ie, it fakes
information that is not really there.
Whether it is good depends strongly on the data, the situation and the
imputation method ("random" often not being a very sensible
choice).
Christian
On Tue, 29 Jul 2008, [EMAIL PROTECTED] wrote:
Hi Paco,
I got the same problem with you before. Thus, I just impute the missing
values
For example:
newdata<-as.matrix(impute(olddata, fun="random"))
then I believe that you could analyze your data.
Hopefully it helps.
Chunhao
Quoting pacomet <[EMAIL PROTECTED]>:
Hello R users
It's some time I am playing with a dataset to do some cluster
analysis. The
data set consists of 14 columns being geographical coordinates and
monthly
temperatures in annual files
latitutde - longitude - temperature 1 -..... - temperature 12
I have some missing values in some cases, maybe there are 8 monthly
valid
values at some points with four non valid. I don't want to supress the
whole
row with 8 good/4 bad values as I wanna try annual and monthy
analysis.
I first tried kmeans but found a problem with missing values. When
trying
without omitting missing values kmeans gives an error and when
excluding
invalid data too many values are excluded in some years of the data
series.
Now I have been reading about pam, pamk and clara, I think they can
handle
missing values. But can't find out the way to perform the analysis
with
these functions. As I'm not an statistics nor an R expert the fpc or
cluster
package documentation is not enough for me. If you know about a
website or a
tutorial explaining the way to use that functions, with examples to
check if
possible, please post them.
Any other help or suggestion is greatly appreciated.
Thanks in advance
Paco
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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
[EMAIL PROTECTED], www.homepages.ucl.ac.uk/~ucakche
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