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 imput
What the best missing value imputation ? It depends on how the values
were generated (e.g. missing at random, informative missing ) and what
type of data (e.g. counts, continuous).
If you are interested in this you could either :
1) take the dataset of complete cases and impute missing values
acc
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 t
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)870 094 086
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 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
>
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
mi
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
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
Bergstresser
> Sent: Wednesday, April 13, 2005 9:37 AM
> To: r-help@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 perfo
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
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