cmdstanr | CmdStanR : the R interface to CmdStan | Development Tools library
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kandi X-RAY | cmdstanr Summary
CmdStanR is a lightweight interface to Stan for R users (see CmdStanPy for Python).
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QUESTION
I have a dataset from participants that provided liking ratings (on a scale from 0-100) of stimuli associated with rewards of different magnitudes (factor pval, with levels small/medium/large) and delay (factor time, with levels delayed/immediate). A subset of the data looks like this:
...ANSWER
Answered 2022-Feb-14 at 22:54Regarding the first question: As is true of most summary methods, the returned object is just a summary, and it doesn't contain the information to convert it back to an object like the one that was summarized. However, the original emmGrid object does have all the needed content.
The other barrier is trying to work from the contrasts you don't want rather than getting the ones you do want. It is usually best to do the means and contrasts in two separate steps. It is quite simple to do:
QUESTION
Is there a way to access the arguments of an R6 method?
For example, I can access the arguments of rstan::stan() with:
ANSWER
Answered 2021-Nov-22 at 19:42A basic feature of R6 is that methods belong to objects. They are not bound in the package namespace, per se, so it is not surprising that you are not able to access them with :: or :::. Here is an example taken directly from ?R6::R6Class:
QUESTION
I am using the Howell1 dataset from rethinking package.
...ANSWER
Answered 2021-Jan-21 at 11:38It turned out that the discrepancy was arising due to seed setting not being honored by brms::posterior_predict
Upon discussing with developer of brms package in github, he root caused the issue to be the following:
If you have set options(mc.cores = ), posterior_predict will evaluate in parallel by default, unless you change the core argument. On windows, parallel execution is done via parallel::parLapply and I don't know how that function respects seeds, if at all. When executing the code in serial (with 1 core) the results are reproducible.
Once I set the mc.cores to 1, I no longer see the discrepancy between posterior_predict and add_predicted_draws.
Hence I am marking the issue as resolved.
The relevant github links are:
QUESTION
I try to run an R script at regular intervals to update a webpage. The script runs fine when called from the terminal like this:
...ANSWER
Answered 2021-Jan-01 at 16:28It looks like I finally managed to fix this, and I'm posting my solution here for anyone who encounters the same problem.
I ran env in terminal to see my current user environment. I copy-pasted the full output to the top of my crontab file. (Simply adding the PATH variable was not sufficient. I suppose it was SHELL or perhaps both PATH and SHELL that did the trick, but I haven't explored this further.)
To edit my user's crontab, I ran crontab -e, then pressed i to edit the file, pasted everything from env at the top of the file, stopped editing by pressing ctrl + c, and quit by typing :wq and hitting enter.
QUESTION
I'm trying to fit a Bayesian model using brms::brm() replacing the prior of one parameter in the model
one after another by purrr::map2() (I have 63 priors for that parameter). I can 'theoretically' save the each fitted model as a distinct object in the global environment (i.e. my workspace) by list2env(), thanks to the answer of my previous question. By list2env(), the objects (brmsfits) would be saved after the all 63 iterations are done. However, when I run the whole code, always I get an error message saying Error in scan(con, nlines = 1, sep = ",", quiet = TRUE) : could not allocate memory (0 Mb) in C function 'R_AllocStringBuffer' and no brmsfit object is stored in the global environment, although the model fitting seems to be done 63 times, as much as my prior exist.
Therefore, I would like to save each brmsfit object as an R object and as an .Rds or .Rda file immediately after its iteration done as to avoid such a memory problem. But, what should I do to realise that?
Note that the following command is just a "schematic" example, with publicly available data, of what I'm trying to do. It will work without the problem I mentioned above, since the data and the model in brm() in this example are way simpler and the number of priors is much fewer than what I'm tackling.
ANSWER
Answered 2020-Oct-29 at 18:53I think you could use something like the following (which is a simpler version that avoids where your data comes from, variable names, model specification, and all that)
QUESTION
I built a simple model using a bernouli distribution in R using cmdstanR.
The stan file:
...ANSWER
Answered 2020-Jan-07 at 10:22That's because you use a logit-Bernoulli distribution.
Then, in the first situation, the posterior concentrates about:
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