zhengp0 / limetr Goto Github PK
View Code? Open in Web Editor NEWRobust linear mixed effects model
License: BSD 2-Clause "Simplified" License
Robust linear mixed effects model
License: BSD 2-Clause "Simplified" License
Dear Professor Zheng,
I have tried to run your example code in docker.
However, the code failed to run in the native environment
Comparing two packages, I have found that limetr package version was inconsistent (docker 0.0.2, verfication releases: 0.0.5)
I wonder if these two versions have any differences,which may cause the code fit_model() not to run
Thanks for making this available! I have found that the import order matters in a surprising way:
If I use
import seaborn
import limetr # needs to be imported before seaborn to work. weird, huh?
I receive the following error
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-1-9f4b0b4257cd> in <module>
1 import seaborn
----> 2 import limetr # needs to be imported before seaborn to work. weird, huh?
/homes/abie/.conda/envs/limetr_conda/lib/python3.7/site-packages/limetr/__init__.py in <module>
1 # nonlinear mixed effects model
2 import numpy as np
----> 3 import ipopt
4 from copy import deepcopy
5 from limetr import utils
/homes/abie/.conda/envs/limetr_conda/lib/python3.7/site-packages/ipopt/__init__.py in <module>
42 from __future__ import absolute_import
43
---> 44 from cyipopt import *
45 from .ipopt_wrapper import minimize_ipopt
46 from .version import __version__
ImportError: dlopen: cannot load any more object with static TLS
If I reverse the order of these imports, it works. This only happens in my Jupyter notebook. In a python interpreter on the command line, I can use either order.
Dear Professor Zheng,
I came across your lately published paper "[Zheng P, Barber R, Sorensen R J D, et al. Trimmed Constrained Mixed Effects Models: Formulations and Algorithms[Z]. 2020.](url: https://arxiv.org/abs/1909.10700)", it is astounding and inspiring. However, I have one question for your paper. In Table 1, you suggest that INLA package cannot deal with "Covariates in random effects variance", and has no "Linear constraints". Yet, it seems to me that the f() function within inla() function can exactly deal with covariates in random effects. Besides, the "extraconstr" argument in inla() can also introduce linear constraints to the fitting model. Thus, I have some doubts for your results.
Also, the link of codes provided by your paper is no longer accessible, could you please share with us your experimental codes for your paper?
Much appreciated,
Dr Xiao Lin
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.