tp_treeppl execute TreePPL and return TreePPL output (string JSON format).
Usage
tp_treeppl(
model = NULL,
model_file_name = "tmp_model_file",
data = NULL,
data_file_name = "tmp_data_file",
compile_model = TRUE,
samples = 1000,
seed = NULL,
n_runs = 1,
method = "smc-bpf",
align = FALSE,
cps = "none",
delay = NULL,
kernel = NULL,
mcmc_lw_gprob = NULL,
pmcmc_particles = NULL,
prune = FALSE,
subsample = NULL,
resample = NULL
)Arguments
- model
a TreePPL model (S3).
- model_file_name
a character vector giving a model name.
- data
a phyjson object (S3).
- data_file_name
a character vector giving a data name.
- compile_model
a base::logical to tell if the model need to be compile
- samples
a base::integer giving the number of samples (mcmc) or particules (smc).
- seed
a base::numeric to use as a random seed.
- n_runs
a base::integer giving the number of run (mcmc)/sweap (smc).
- method
a character vector giving the inference method name.
- align
a base::logical to tell if need to align the model.
- cps
a character vector giving the configuration of CPS transformation.
- delay
a character vector giving the configuration of delayed sampling.
- kernel
a base::numeric value giving the driftScale for driftKernel in MCMC.
- mcmc_lw_gprob
a base::numeric probability of performing a global MCMC step.
- pmcmc_particles
a base::integer number of particles for the smc proposal computation
- prune
a base::logical to tell if the model will try to be pruned.
- subsample
a base::integer number of draw to subsample from the posterior distribution.
- resample
a character vector giving the selected resample placement method
Details
This function takes TreePPL object (S3) and phyjson object (S3), compile TreePPL model, run it with data and returning TreePPL output.
TreePPL need to be install on your computer and the PATH set for R/RSTUDIO
(see install manual).
The executable and the output files will be written in R's base::tempdir().
model : A TreePPL model (S3), see tp_model for further details.
Use 'NULL' if you have previously provide an model. Check already provide
model with tp_model_stored.
model_file_name : a character vector giving to tp_treeppl as
a model name. Use a tp_data_stored name if you have already
write your model with tp_treeppl.
data : A phyjson object (S3), see tp_phyjson() for further
details. Use 'NULL' if you have previously provide data. Check already
provide data with tp_data_stored.
data_file_name : a character vector giving to tp_treeppl
a data name. Use a tp_data_stored name if you have already write
your data with tp_treeppl.
compile_model : a base::logical telling if the model need to be compiled.
Can be use to avoid to compile a model again in R's base::tempdir()
if you have already compile a model in a previous call of
tp_treeppl. Check already compile model
with tp_compile_stored.
samples : The number of samples (mcmc) / particules (smc) during inference.
seed : The random seed to use. Using 'NULL' initialized randomly.
n_runs : The number of run (mcmc) / sweap (smc) used for the inference.
method : Inference method to be used. The selected inference method.
The supported methods are: is-lw, smc-bpf, smc-apf, mcmc-lightweight,
mcmc-trace, mcmc-naive, pmcmc-pimh.
The following options are highly dependable of the method used. Check [not implemented yet] for more information.
align : Whether or not to align the model for certain inference algorithms.
cps : Configuration of CPS transformation (only applicable to certain
inference algorithms). The supported options are: none, partial, and full.
delay : The model is transformed to an efficient representation if
possible. The supported options are: static or dynamic. Use 'NULL' to ignore.
kernel : The value of the driftScale for driftKernel in MCMC. Use 'NULL'
to ignore. Use in conjuction with method mcmc-lightweight".
Use 'NULL' to ignore
mcmc_lw_gprob : The probability of performing a global MH step
(non-global means only modify a single sample in the previous trace).
Use in conjuction with method mcmc-lightweight". Use 'NULL' to ignore
pmcmc_particles : The number of particles for the smc proposal computation.
This option is used if one of the following methods are used: pmcmc-*.
Use 'NULL' to ignore
prune : The model is pruned if possible.
subsample : The number of draw to subsample from the posterior
distribution. Use in conjuction with method smc-apf or smc-bpf.
Use 'NULL' to ignore.
resample: The selected resample placement method, for inference algorithms
where applicable. The supported methods are:
likelihood (resample immediately after all likelihood updates),
align (resample after aligned likelihood updates, forces –align),
and manual (sample only at manually defined resampling locations).
Use 'NULL' to ignore.