bdiv_stats: Test beta diversity for associations with metadata.

In rbiom: Read/Write, Analyze, and Visualize 'BIOM' Data

Test beta diversity for associations with metadata.

Description

A convenience wrapper for bdiv_table() + stats_table().

Usage

bdiv_stats(
  biom,
  regr = NULL,
  stat.by = NULL,
  bdiv = "Bray-Curtis",
  weighted = TRUE,
  tree = NULL,
  within = NULL,
  between = NULL,
  split.by = NULL,
  transform = "none",
  test = "emmeans",
  fit = "gam",
  at = NULL,
  level = 0.95,
  alt = "!=",
  mu = 0,
  p.adj = "fdr"
)

Arguments

biom	An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.
regr	Dataset field with the x-axis (independent; predictive) values. Must be numeric. Default: NULL
stat.by	Dataset field with the statistical groups. Must be categorical. Default: NULL
bdiv	Beta diversity distance algorithm(s) to use. Options are: "Bray-Curtis", "Manhattan", "Euclidean", "Jaccard", and "UniFrac". For "UniFrac", a phylogenetic tree must be present in biom or explicitly provided via ⁠tree=⁠. Multiple/abbreviated values allowed. Default: "Bray-Curtis"
weighted	Take relative abundances into account. When weighted=FALSE, only presence/absence is considered. Multiple values allowed. Default: TRUE
tree	A phylo object representing the phylogenetic relationships of the taxa in biom. Only required when computing UniFrac distances. Default: biom$tree
within, between	Dataset field(s) for intra- or inter- sample comparisons. Alternatively, dataset field names given elsewhere can be prefixed with '==' or '!=' to assign them to within or between, respectively. Default: NULL
split.by	Dataset field(s) that the data should be split by prior to any calculations. Must be categorical. Default: NULL
transform	Transformation to apply. Options are: c("none", "rank", "log", "log1p", "sqrt", "percent"). "rank" is useful for correcting for non-normally distributions before applying regression statistics. Default: "none"
test	Method for computing p-values: 'wilcox', 'kruskal', 'emmeans', or 'emtrends'. Default: 'emmeans'
fit	How to fit the trendline. 'lm', 'log', or 'gam'. Default: 'gam'
at	Position(s) along the x-axis where the means or slopes should be evaluated. Default: NULL, which samples 100 evenly spaced positions and selects the position where the p-value is most significant.
level	The confidence level for calculating a confidence interval. Default: 0.95
alt	Alternative hypothesis direction. Options are '!=' (two-sided; not equal to mu), '<' (less than mu), or '>' (greater than mu). Default: '!='
mu	Reference value to test against. Default: 0
p.adj	Method to use for multiple comparisons adjustment of p-values. Run p.adjust.methods for a list of available options. Default: "fdr"

Value

A tibble data.frame with fields from the table below. This tibble object provides the ⁠$code⁠ operator to print the R code used to generate the statistics.

Field	Description
.mean	Estimated marginal mean. See emmeans::emmeans().
.mean.diff	Difference in means.
.slope	Trendline slope. See emmeans::emtrends().
.slope.diff	Difference in slopes.
.h1	Alternate hypothesis.
.p.val	Probability that null hypothesis is correct.
.adj.p	.p.val after adjusting for multiple comparisons.
.effect.size	Effect size. See emmeans::eff_size().
.lower	Confidence interval lower bound.
.upper	Confidence interval upper bound.
.se	Standard error.
.n	Number of samples.
.df	Degrees of freedom.
.stat	Wilcoxon or Kruskal-Wallis rank sum statistic.
.t.ratio	.mean / .se
.r.sqr	Percent of variation explained by the model.
.adj.r	.r.sqr, taking degrees of freedom into account.
.aic	Akaike Information Criterion (predictive models).
.bic	Bayesian Information Criterion (descriptive models).
.loglik	Log-likelihood goodness-of-fit score.
.fit.p	P-value for observing this fit by chance.

See Also

Other beta_diversity: bdiv_boxplot(), bdiv_clusters(), bdiv_corrplot(), bdiv_heatmap(), bdiv_ord_plot(), bdiv_ord_table(), bdiv_table(), distmat_stats()

Other stats_tables: adiv_stats(), distmat_stats(), stats_table(), taxa_stats()

Examples

    library(rbiom)
    
    biom <- rarefy(hmp50)
      
    bdiv_stats(biom, stat.by = "Sex", bdiv = c("bray", "unifrac"))[,1:7]
    
    biom <- subset(biom, `Body Site` %in% c('Saliva', 'Stool', 'Buccal mucosa'))
    bdiv_stats(biom, stat.by = "Body Site", split.by = "==Sex")[,1:6]

rbiom documentation built on June 28, 2025, 1:07 a.m.