Comments (3)
I see your point but:
-
in most cases it is more interesting to see the distribution of the iterations that the overall iterations (which is specified by the user).
-
this would break a lot of my code since I use the
mstop()
function a lot in the internal fitting process.
What we could do is if mstop(model) <- c(mu = 10, sigma = 20)
is called we use sum(c(mu = 10, sigma = 20))
as new mstop and give a warning to the user?
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I see. Let's discuss this at the phone. That is easier.
from gamboostlss.
return scalar with one attribute for individual steps per distribution parameter. perhaps add nice print function...
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Related Issues (20)
- Prepare new release version HOT 5
- risk(, merge = TRUE) broken for method = cyclic
- selected.mboostLSS is broken HOT 1
- stabsel is broken HOT 4
- Delete stco_paper branch HOT 3
- summary() fails if no base-learner was selected HOT 2
- Update References HOT 2
- cvrisk.nc_gamboostLSS exportation and default HOT 2
- Update reference to Thomas et al HOT 2
- predict for (some) noncyclic models broken HOT 6
- Scoping problem with "combined_risk" in nc_mboostLSS HOT 9
- stabsel.mboostLSS does not work on FDboost with scalar response HOT 3
- mboost_LSS does not work for "matrix valued, but scalar" responses fit with FDboost HOT 4
- example code in families.Rd failes HOT 4
- Add option for stochastic gradient descent HOT 1
- predint() not working when multiple variables are included within a single baselearner HOT 3
- Problem on calculating risk in gamboostLSS with noncyclical approach HOT 1
- Error when predicting with large N HOT 1
- Problem fitting a blackboost(LSS) model to toy data
- Package not available via CRAN HOT 1
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