I think I just answered my own question.
The scale function will return the mean and sd of the data.
So the process is fairly simple.
scale training data varaible
note mean and sd from the scale
then manually scale the test data using the mean and sd from the
training data.
That should make sure that a value is transformed the same regardless of
which data set it is in.
Do I have this correct, or can anybody contribute any more to the concept?
Thanks!
--
Noah
On 9/11/09 1:10 PM, Noah Silverman wrote:
Hi,
Is there an alternative to the scale function where I can specify my
own mean and standard deviation?
I've come across an interesting issue where this would help.
I'm training and testing on completely different sets of data. The
testing set is smaller than the training set.
Using the standard scale function of R seems to introduce some error.
Since it scales data WITHIN the set, it may scale the same number to
different value since the range in the training and testing set may be
different.
My thought was to scale the larger training set of data, then use the
mean and SD of the training data to scale the testing data according
to the same parameters. That way a number will transform to the same
result regardless of whether it is in the training or testing set.
I can't be the first one to have looked at this. Does anyone know of
a function in R or if there is a scale alternative where I can control
the parameters?
Thanks!
--
Noah
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