On Jun 23, 2009, at 10:25 AM, sunny vic wrote:
Hi everyone.
Probably this is statistical question rather than an R, but it
involves
packages from R I am asking here since I am unable to find an
answer. In the
parametric modeling packages like glmnet, lasso etc......., we are
able to
obtain the coeffcients that have entered the model.
for eg in glmnet if we are working on a dataset containing 15
variables
the coeffecient parameters output is like this, from the below
result we
know that 5 variables or features have entered the model and are
chosen and
the rest 10 variables have not entered, can we plot an ROC curve
detremine
sensitivity, specificity and confusion matrix using just this below
information. any input would be great.
0.000
0.01213
-0.1213
0.0000
0.0000
0.0000
0.0000
-0.00034
0.0000
0.0000
0.0000
0.0000
0.0023
0.0988
0.0000
No. ROC's require a more complete picture of the data and model
predictions than just the model coefficients. Because that was not
clear, I worry about what else you may be doing with those packages.
Besides the warnings about GIGO, it's also possible to make garbage
out of perfectly good inputs by applying faulty methods. In either
case, an intact sense of smell is essential.
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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