On 04/13/05 21:05, Chris Bergstresser wrote:
This article is great; thanks for providing it. The authors
recommend either using ML Estimation or Multiple Imputation to fill
in the missing data. They don't talk much about which is better for
certain situations, however.
Multiple
Hi all --
I've got a large dataset which consists of a bunch of different
scales, and I'm preparing to perform a cluster analysis. I need to
normalize the data so I can calculate the difference matrix.
First, I didn't see a function in R which does normalization -- did
I miss it? What's
@stat.math.ethz.ch
Subject: [R] Normalization and missing values
Hi all --
I've got a large dataset which consists of a bunch of different
scales, and I'm preparing to perform a cluster analysis. I need to
normalize the data so I can calculate the difference matrix.
First, I didn't see
Bert Gunter wrote:
You can't expect statistical procedures to rescue you from poor
data.
That should ***definitely*** go into the fortune package
data base!!!
cheers,
Rolf Turner
On 04/13/05 11:36, Chris Bergstresser wrote:
Hi all --
I've got a large dataset which consists of a bunch of different
scales, and I'm preparing to perform a cluster analysis. I need to
normalize the data so I can calculate the difference matrix.
First, I didn't see a function in R
before I know the scale() function, I just do it by coding it myself.
But probably you could find some cool stuffs in dprep library. I've
never tried it anyway.
for missing values, it is way more complex and also depends on the
methodology you are going to use. some methods are more tolerant to
On Wed, 13 Apr 2005 14:33:25 -0300 (ADT) Rolf Turner wrote:
Bert Gunter wrote:
You can't expect statistical procedures to rescue you from poor
data.
That should ***definitely*** go into the fortune package
data base!!!
:-) added for the next release.
Z
the way of scaling, IMHO, really depends on the distribution of each
column in your original files. if each column in your data follows a
normal distrbution, then a standard normalization will fit your
requirement.
My previous research in microarray data shows me a simple linear
standardization
On 13-Apr-05 Berton Gunter wrote:
You can't expect statistical procedures to rescue you from
poor data.
But they can kiss it better.
(:-x)
Ted.
E-Mail: (Ted Harding) [EMAIL PROTECTED]
Fax-to-email: +44 (0)870 094 0861
I'd just like to thank everyone who wrote in in response to my
questions -- it's been greatly helpful, and appreciated.
Jonathan Baron wrote:
On 04/13/05 11:36, Chris Bergstresser wrote:
First, I didn't see a function in R which does normalization -- did
I miss it? What's the best way
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