So does anyone use this package?
- Original Message -
From: Scott Raynaud scott.rayn...@yahoo.com
To: r-help@r-project.org r-help@r-project.org
Cc:
Sent: Tuesday, January 10, 2012 1:40 PM
Subject: grplasso
I want to use the grplasso package on a data set where I want to fit a linear
model. My interest is in identifying significant beta coefficients. The
documentation is a bit cryptic so I'd appreciate some help.
I know this is a strategy for large numbers of variables but consider a simple
case for pedagogical puposes. Say I have two 3 category predictors (2 dummies
each), a binary predictor and a continuous predictor with a continuous outcome:
y x1 x2 x3 x4 x5 x6
rows of data here
..
..
Naturally, I want to select x1 and x2 as a group and x3 and x4 as another
group.
The documentation has a couple of examples but it's not clear how they
translate
to the current problem. How do I specify my groups and run the lasso
regression?
Looks like this is the grouping part:
index-c(NA,)
but I'm not sure how to specify the df for the variables past the NA for the
intercept.
Once that's defined the penalty can be specified:
lambda - lambdamax(x, y = y, index = index, penscale = sqrt,
model = LogReg()) * 0.5^(0:5)
In my case I'd use LinReg for the model.
Then the model:
fit - grplasso(x, y = y, index = index, lambda = lambda, model = LogReg(),
penscale = sqrt, control = grpl.control(update.hess = lambda, trace = 0))
again using LinReg for the model.
This can be plotted against lambda, but when I do lasso regression
in other software I end up with a plot of the coefficients against the
tuning parameter with a cutpoint or a table and graph that tells me
what to include in the model based on some selected criterion.
It's not clear from the example if there's a cross-validation or some
other procedure to determine what variables to include. Plot(fit)
produces a graph of coefficients against lambda but nothig to indicate
what to include. What is used in the package, if anything, to make that
determination?
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