Thank you, Bert. I'll definitely ask there.
In the meantime I just wanted to ensure that my R code (my function for
bootstrap and the bootstrap run) is correct and my abnormal bootstrap
results are not a function of my erroneous code.
Thank you!
On Mon, Jan 27, 2014 at 7:09 PM, Bert Gunter
Here is a great response I got from SO:
There is an important difference between the two importance measures:
MeanDecreaseAccuracy is calculated using out of bag (OOB) data,
MeanDecreaseGini is not. For each tree MeanDecreaseAccuracy is calculated
on observations not used to form that particular
I think that the fundamental problem is that you are using the default
value of ntree (500). You should always use at least 1500 and more if n or
p are large.
Also, this link will give you more up-to-date information on that package
and feature selection:
Hello!
Below, I:
1. Create a data set with a bunch of factors. All of them are predictors
and 'y' is the dependent variable.
2. I run a classification Random Forests run with predictor importance. I
look at 2 measures of importance - MeanDecreaseAccuracy and MeanDecreaseGini
3. I run 2 boostrap
I **think** this kind of methodological issue might be better at SO
(stats.stackexchange.com). It's not really about R programming, which
is the main focus of this list. And yes, I know they do intersect.
Nevertheless...
Cheers,
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650)
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