sim_reg: Similarity regression
In SimReg: Similarity Regression

Performs Bayesian ‘similarity regression’ on given logical response vector y against list of ontological term sets x. It returns an object of class sim_reg_output. Of particular interest are the probability of an association, which can be calculated with prob_association, and the characteristic ontological profile phi, which can be visualised using the functions plot_term_marginals, and term_marginals). The results can be summarised with summary.

sim_reg(
  ontology,
  x,
  y,
  information_content = get_term_info_content(ontology, x),
  sim_params = list(ontology = ontology, information_content = information_content),
  using_terms = get_terms(sim_params),
  term_weights = rep(0, length(using_terms)),
  prior = discrete_gamma(using_terms),
  min_BF = -Inf,
  max_select = 2000L,
  max_phi_count = 200L,
  two_way = TRUE,
  selection_fn = fg_step_tab(N = length(y)),
  lik_method = NULL,
  lik_method_args = list(),
  gamma0_ml = bg_rate,
  min_ratio = 1e-04,
  ...
)

`ontology`	`ontology_index` object.
`x`	`list` of `character` vectors of ontological terms.
`y`	`logical` response vector.
`information_content`	Numeric vector of information contents of terms named by term ID. Defaults to information content based on frequencies of annotation in `x`.
`sim_params`	List of arguments to pass to `get_asym_sim_grid`.
`using_terms`	Character vector of term IDs giving the complete set of terms to include in the the `phi` parameter space.
`term_weights`	Numeric vector of prior weights for individual terms.
`prior`	Function for computing the unweighted prior probability of a `phi` value.
`min_BF`	Bayes factor threshold below which to terminate computation, enabling faster execution time at the expense of accuracy and precision.
`max_select`	Upper bound for number of `phi` values to sample.
`max_phi_count`	Upper bound for number of `phi` values to include in final likelihood sum.
`two_way`	Boolean value determining whether to calculate semantic similarity ‘in both directions’ (i.e. compute `s_x` and `s_phi` or just `s_phi`).
`selection_fn`	Function for selecting values of `phi` with high posterior mass.
`lik_method`	Function for calculating marginal likelihood contional on values of `phi`.
`lik_method_args`	List of additional arguments to pass to `lik_method`.
`gamma0_ml`	Function for computing marginal likelihood of data under baseline model `gamma=0`.
`min_ratio`	Lower bound on ratio below which to discard `phi` values.
`...`	Additional arguments to pass to `selection_fn`.

## Not run: 
set.seed(0)
data(hpo)
disease_terms <- c("HP:0005537", "HP:0000729", "HP:0001873")
all_terms <- get_ancestors(hpo, 
c(disease_terms, sample(hpo$id, size=50)))
y <- c(rep(FALSE, 96), rep(TRUE, 3))
x <- lapply(y, function(.y) minimal_set(
hpo, if (!.y) sample(all_terms, size=3) else 
	c(sample(all_terms, size=1), disease_terms[runif(n=3) < 0.8])))
sim_reg_out <- sim_reg(ontology=hpo, x=x, y=y)

## End(Not run)