Comments (5)
OK, all those families have FAM()$type == "Mixed"
, found that in my own code.
Apparently I had already implemented such cases for 3-parametric families and was only too lazy to do the same also for the 4-paremetric ones. Should be fixed now in "devel".
from gamboostlss.
Thanks for finding this! This should be a problem for all inflated families, e.g. BEOI()
:
BEINF()$nopar
[1] 4
BEOI()$nopar
[1] 3
But their corresponding functions for the derivatives BEOI()$dldm
have arguments mu and sigma only which leads to the error.
I think we should be able to fix this in our code, I'll take a look.
from gamboostlss.
from gamboostlss.
Thanks a lot!
Perhaps there are even more families with similar properties?
Could check this issue by comparing nopar
with the number of parameters in dldm
either by simply calling the functions or by computing on
deparse(gamlss.dist::as.gamlss.family("BEINF")$dldm)[1]
for all available families in a loop?
from gamboostlss.
Merci :)
from gamboostlss.
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
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from gamboostlss.