https://github.com/johannesbuchner/rmultinest
R wrapper for MultiNest
https://github.com/johannesbuchner/rmultinest
bayesian-inference integration-methods monte-carlo nested-sampling r
Last synced: 7 months ago
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R wrapper for MultiNest
- Host: GitHub
- URL: https://github.com/johannesbuchner/rmultinest
- Owner: JohannesBuchner
- Created: 2012-08-21T00:08:32.000Z (about 13 years ago)
- Default Branch: master
- Last Pushed: 2015-05-04T21:08:23.000Z (over 10 years ago)
- Last Synced: 2025-01-23T20:51:34.935Z (9 months ago)
- Topics: bayesian-inference, integration-methods, monte-carlo, nested-sampling, r
- Language: C++
- Size: 148 KB
- Stars: 3
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
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README
==========================
R Bridge for MultiNest
==========================
Author: Johannes Buchner (C) 2012-2013
About
---------------------
This code allows likelihood functions written in R (http://www.r-project.org) to be used by MultiNest.
How does it work
---------------------
The main program will connect to R using Rserve. It will run MultiNest, and
for each point evaluation call the user-defined R callback function.
Installation
---------------------------
#. You need MultiNest.
* Download it (e.g. from https://github.com/JohannesBuchner/MultiNest)
* Make sure libmultinest.so is in your library path if it is not already::
$ export LD_LIBRARY_PATH=/my/path/to/MultiNest/
.. warning::
If you do not do this, you will see this error::
./rbridge: error while loading shared libraries: libmultinest.so: cannot open shared object file: No such file or directory
#. Download this package (latest at https://github.com/JohannesBuchner/RMultiNest), and extract it into the MultiNest directory.
.. hint::
Quick installation::
$ R --no-save
> install.packages("Rserve")
> quit()
$ make rbridge test
If that doesn't go through smoothly, follow the manual steps 2-6.
Please report issues at https://github.com/JohannesBuchner/RMultiNest/issues
Building Rserve C++ client
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Otherwise, follow these manual steps.
2. Download Rserve (the tar file, Rserve_xxxx.tar.gz) from http://www.rforge.net/Rserve/ and extract it into this directory. You should now have::
$ ls
Makefile
README.rst
multinest.h
rbridge.cc
Rserve/
3. Configure Rserve::
$ ./Rserve/configure
4. Configure and build the Rserve C++ client (in Rserve/clients/cxx/)::
$ cd Rserve/clients/cxx/
$ ./configure && make Rconnection.o
$ cd ../../../
Building Rbridge
~~~~~~~~~~~~~~~~~~~~~~~~~~~
5. Run make to compile Rbridge::
$ make
Installing Rserve in R
~~~~~~~~~~~~~~~~~~~~~~~~~~~
6. In R, install RServe using the instructions on the website. In short::
$ R
> install.packages("Rserve")
Running
---------------------
The "runtest.sh" script runs the steps below for testing whether the installation worked.
But you probably want to understand how to run your own code, so follow these steps.
1. Write log likelihood function in a R file (has to be called "log_likelihood"),
and a prior transformation (has to be called "prior")
test.r::
prior <- function(cube) cube
log_likelihood <- function(params) {
print (params);
0
}
This is an example flat log_likelihood, and a flat prior.
2. Write a config file that tells which file to load
conf.rs::
source test.r
3. Run Rserve
::
$ R
> library(Rserve) # you installed this package before using install.packages
> Rserve(args=c("--RS-conf", "conf.rs", "--no-save"))
4. run ./rbridge in shell
::
$ ./rbridge
This will connect to R using Rserve, and call R through that for each point evaluation.
5. Finally, terminate Rserve
::
$ killall Rserve-bin.so
What now?
~~~~~~~~~~
Well, you should now have the MultiNest output files lying there. You can learn to understand
them by reading the MultiNest README (https://github.com/JohannesBuchner/MultiNest/blob/master/README).
You can either use pymultinest to plot and analyse them, or write your own routines in R.