Open tdhock opened 5 years ago
tell the user that they can specify manually the vector of fold ID numbers in cv.iregnet
that is the foldid
argument in my PR https://github.com/anujkhare/iregnet/pull/54/files#diff-028243aa200892f66756ed85c2d1ede7R82
We need to come up with a time for each week when everyone will be available so that we can discuss on what all has been accomplished and what needs to be done next.
tell the user that they can specify manually the vector of fold ID numbers in cv.iregnet
Okay, noted
I agree with @anujkhare about the optimization branch, let's not merge it until the code is clean / easy to maintain / no duplication.
16 May 2019 linear regression y_i ~ N( w^T x_i, \sigma^2) log t_i = y_i y_i ~ N( f(x_i), \sigma^2) f(x_i) = w^T x_i f(x_i) = b_0 + b^T x_i uncensored y_i => likelihood is density censored y_i => likelihood is cumulative distribution function
Aditya said that NAN error in iregnet only happens when train labels have no lower limits or no upper limits. in this case the max likelihood model is undefined, so you should stop with an error that tells the user that at least one upper limit and one lower limit are required.
cv.iregnet: you should check to make sure that each train set/split has at least one upper and one lower limit, otherwise stop with an informative error before running the optimization/iregnet. e.g. here is the error message I use in penaltyLearning::IntervalRegressionCV, which tells the user which https://github.com/tdhock/penaltyLearning/blob/master/R/IntervalRegression.R#L189