Tables of a path-anomaly (HYPA) fit

Usage

# S3 method for class 'hypernets_hypa'
hg_get(x, ...)

# S3 method for class 'net_hypa'
hg_get(
  x,
  what = c("anomalies", "scores", "over", "under", "pathways"),
  ...,
  type = NULL,
  order_by = NULL,
  top = NULL
)

Arguments

x: A net_hypa object from hypa() on sequences.
...: Unused.
what: "anomalies" (default) for the anomalous paths selected by type, "scores" for every scored path, "over" or "under" for the significantly over- or under-represented paths with all their columns, or "pathways" for the anomalous paths written as pathways ("a b -> c": the context states, then the next state), the form simplicial() turns into simplices.
type: For "anomalies" and "pathways": "all", "over" or "under". Default: the type given to hypa(), else "all".
order_by: "sig" (smallest p-value of the path's own direction first), "ratio" (largest deviation first: highest ratio for over-represented paths, lowest for under-represented ones, largest absolute log ratio when both directions are listed), "freq" (most observed first) or "path" (alphabetical). Default: the order_by given to hypa(), else "sig". For "scores", "sig" sorts by p_value.
top: Integer or NULL. Return only the first top rows, applied after any filter and after order_by. Default NULL returns every row.

Value

A data.frame. For "anomalies", one row per anomalous path: path, count, expected, ratio, p_adj (the adjusted p-value of the path's own direction), direction, and order when several orders were scored. For "scores", "over" and "under", one row per path with every column of the fit: path, from, to, count, expected, ratio, the p-values (p_value, p_under, p_over, p_adj_under, p_adj_over), the direction ("over", "under" or "normal") and the path order. For "pathways", one row per anomalous path with the column pathway.

Examples

seqs <- list(c("a", "b", "c", "a", "b", "c"), c("x", "b", "d", "x", "b", "d"),
             c("a", "b", "c", "a", "b", "c"), c("x", "b", "d", "x", "b", "d"))
fit <- hypa(seqs, order = 2, min_count = 1)
hg_get(fit, type = "over", order_by = "ratio", top = 5)
#> [1] path      count     expected  ratio     p_adj     direction
#> <0 rows> (or 0-length row.names)