On 07/23/2011 11:43 AM, fongchun wrote:
I was also thinking of a bootstrapping approach where I would actually run
cv.glmnet say 100 times and then take the mean/median lambda across all the
cv.glmnet runs. This way I generate a confidence interval for my optimal
lambda I woud use in the end.
10 fold cv has high variation compared to other methods. Use repeated cv or the
bootstrap instead (both of which can be used with glmnet by way of the train()
function on the caret package).
Max
On Jul 23, 2011, at 11:43 AM, fongchun wrote:
> Hi Patrick,
>
> Thanks for the reply. I am ref
Hi Patrick,
Thanks for the reply. I am referring to using the cv.glmnet() function with
10-fold cross validation and letting glmnet determine the lambda sequence.
The optimal lambda that it is returning fluctuates between different runs of
cv.glmnet. Sometimes the model that is return deviates
On 07/22/2011 07:51 PM, fongchun wrote:
I am using the glmnet R package to run LASSO with binary logistic
regression.
...
What I am finding is that this optimal lambda value fluctuates
everytime I run glmnet with LASSO.
> ...
Does anyone know why there is such a fluctuation in the
generation o
Hi all,
I am using the glmnet R package to run LASSO with binary logistic
regression. I have over 290 samples with outcome data (0 for alive, 1 for
dead) and over 230 predictor variables. I currently using LASSO to reduce
the number of predictor variables.
I am using the cv.glmnet function to d
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