fit_GP: Fit a Gaussian process to a single host series using basset
In kimberlyroche/ROL: What the package does (short line)

Description Usage Arguments Details Examples

Fit a Gaussian process to a single host series using basset

fit_GP(
  data,
  host,
  taxa_covariance,
  sample_covariance,
  tax_level = "ASV",
  alr_ref = NULL,
  n_samples = 100,
  MAP = FALSE,
  holdout_proportion = 0,
  return_model = FALSE,
  scramble = FALSE
)

`data`	a phyloseq object
`host`	host short name (e.g. ACA)
`taxa_covariance`	list of prior covariance parameters over taxa
`sample_covariance`	kernel function
`tax_level`	taxonomic level at which to agglomerate data
`alr_ref`	index of reference ALR coordinate
`n_samples`	number of posterior samples to draw
`MAP`	compute MAP estimate only (as single posterior sample)
`holdout_proportion`	if non-zero, proportion of host's sample to use as a test set
`return_model`	if TRUE, returns the fitted model instead of saving it
`scramble`	if TRUE, host samples are scrambled in time; this serve the diagnostic purpose of identifying whether some of the correlation we see between individual hosts' dynamics are the result of systematic differences in abundances (etc.)
`rho`	bandwidth for SE kernel

Fitted model and metadata saved to designated model output directory.

tax_level <- "ASV"
data <- load_data(tax_level = tax_level)
params <- formalize_parameters(data)
sample_covariance <- get_Gamma(kernel_scale = 2, proportions = c(1, 0, 0), min_correlation = 0.1, days_to_baseline = 90)
taxa_covariance <- get_Xi(phyloseq::ntaxa(data), total_variance = 1)
fit_GP(data, host = "GAB", taxa_covariance = taxa_covariance, sample_covariance = sample_covariance, tax_level = tax_level, alr_ref = params$alr_ref, MAP = TRUE)