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Hi, before I get into this issue report, I wanted to thank you for writing such a useful package :)
I have been using stabsel() with glmnet() and noticed the following on line 126 of glmnet.lasso_maxCoef: fit <- glmnet::glmnet(x, y, ...)
fit <- glmnet::glmnet(x, y, ...)
glmnet.lasso_maxCoef() does not seem to be using the penalty parameter lambda= that is passed to it.
glmnet.lasso_maxCoef()
lambda=
I was expecting line 126 to look like:
fit <- glmnet::glmnet(x, y, lambda=lambda, ...)
Assuming the lambda value passes the check on line 120, is it being captured by the ...? If not this looks like a bug.
...
Thanks Peter
PS: ?glmnet suggests using predict.glmet() for single lambda values.
?glmnet
predict.glmet()
The text was updated successfully, but these errors were encountered:
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Hi,
before I get into this issue report, I wanted to thank you for writing such a useful package :)
I have been using stabsel() with glmnet() and noticed the following on line 126 of glmnet.lasso_maxCoef:
fit <- glmnet::glmnet(x, y, ...)
glmnet.lasso_maxCoef()
does not seem to be using the penalty parameterlambda=
that is passed to it.I was expecting line 126 to look like:
fit <- glmnet::glmnet(x, y, lambda=lambda, ...)
Assuming the lambda value passes the check on line 120, is it being captured by the
...
? If not this looks like a bug.Thanks
Peter
PS:
?glmnet
suggests usingpredict.glmet()
for single lambda values.The text was updated successfully, but these errors were encountered: