resume: Resume HMC using a previous fit

In sw1/themetagenomics: Exploring Thematic Structure and Predicted Functionality of 16s rRNA Amplicon Data

Description

Perform HMC using a previously compiled Stan model. This is specifically useful in cases when a previous fit failed to converged (i.e., Rhat > 1.1 for a portion of parameter estimates), thus requiring more iterations.

Usage

resume(object, ...)

## S3 method for class 'effects'
resume(
  object,
  init_type = c("last", "orig"),
  inits,
  iters,
  warmup = iters/2,
  chains = 1,
  cores = 1,
  seed = object$seeds$next_seed,
  return_summary = TRUE,
  verbose = FALSE,
  ...
)

Arguments

object	(required) Ouput of est.functions.
...	Additional arguments for methods.
init_type	Type of initial parameters, either the original set that was passed to est.functions or the last parameter sample from the reused fit. Defaults to last.
inits	List of values for parameter initialization. Overrides init_type.
iters	Number of iterations for for fitting. Defaults to 300 and 100 for HMC and ML, respectively.
warmup	For HMC, proportion of iterations devoted to warmup. Defaults to iters/2.
chains	For HMC, number of parallel chains. Defaults to 1.
cores	For HMC, number of cores to parallelize chains. Defaults to 1.
seed	Seed for the random number generator to reproduce previous results.
return_summary	Logical flag to return results summary. Defaults to TRUE.
verbose	Logical flag to print progress information. Defaults to FALSE.

Value

An object of class effects containing

model

List containing the parameters, fit, and summary.

gene_table

Dataframe containing the formatted predicted gene information from predict.topics.

References

Stan Development Team. 2016. RStan: the R interface to Stan. http://mc-stan.org

See Also

stan est.functions

Examples

formula <- ~DIAGNOSIS
refs <- 'Not IBD'

dat <- prepare_data(otu_table=GEVERS$OTU,rows_are_taxa=FALSE,tax_table=GEVERS$TAX,
                    metadata=GEVERS$META,formula=formula,refs=refs,
                    cn_normalize=TRUE,drop=TRUE)

## Not run: 
topics <- find_topics(dat,K=15)

functions <- predict(topics,reference_path='/references/ko_13_5_precalculated.tab.gz')

function_effects_init <- est(functions,level=3,iters=150,
                             prior=c('laplace','t','laplace'))
function_effects <- resume(function_effects_init,init_type='last',
                           iters=300,chains=4)

## End(Not run)

sw1/themetagenomics documentation built on May 24, 2020, 8:37 p.m.