Comments (2)
Thanks for reporting this bug! In the case of the real dataset was there a single solution in the path?
For some reason, when there's a single solution in the path, the package we're using to interface C++ with R is storing fit$beta[[1]] as a dense vector. However, fit$beta[[1]] is supposed to be a sparse vector -- coef expects sparse vectors. I'll fix this and update the package later today
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The bug is now fixed in the latest version (1.0.9). This can be installed directly from Github using
library(devtools)
install_github("hazimehh/L0Learn")
The latest version should be available on CRAN in the next few days.
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Related Issues (16)
- Readme images
- outputs for L0learn.cvfit not documented HOT 2
- OpenMP not found on MAC OS HOT 2
- Control of lambda0 and the corresponding support size? HOT 2
- Clarification on the solution path HOT 1
- Control of number of lambda values in L0Learn.fit with algorithm = "CDPSI" HOT 3
- L0 and L0L2 results from the CDPS(1) method are confusing HOT 6
- Tuning the parameter, gamma, using the function L0Learn.cvfit HOT 2
- L1L2 and L0L1L2 HOT 1
- Excessive memory usage in L0Learn.cvfit with large n, small p HOT 1
- Pre-specifying lambda grid does not seem to work HOT 5
- Allowing for positivity or box constraints on fitted coefficients? HOT 2
- Prediction results on test data from L0 is much worse than L0L2 or LASSO HOT 2
- Allow for observation weights to be able to fit e.g. identity link Poisson GLMs (with or without nonnegativity constraints) HOT 2
- Support for sparse covariate matrices? HOT 1
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