Test beta diversity for associations with metadata.

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.

Examples

    library(rbiom)
    
    biom <- rarefy(hmp50)
      
    bdiv_stats(biom, stat.by = "Sex", bdiv = c("bray", "unifrac"))[,1:7]
#> # Model:    gam(.distance ~ Sex, method = "REML")
#> # A tibble: 6 × 7
#>   .bdiv       Sex                   .mean.diff .h1    .p.val .adj.p .effect.size
#>   <fct>       <chr>                      <dbl> <fct>   <dbl>  <dbl>        <dbl>
#> 1 Bray-Curtis Female - Male            0.0599  != 0  0.00402 0.0219      0.261  
#> 2 Bray-Curtis Male - Female vs Male   -0.0489  != 0  0.00730 0.0219     -0.235  
#> 3 UniFrac     Female - Female vs M…   -0.0259  != 0  0.0866  0.173      -0.109  
#> 4 UniFrac     Male - Female vs Male   -0.0249  != 0  0.187   0.281      -0.115  
#> 5 Bray-Curtis Female - Female vs M…    0.0110  != 0  0.430   0.515       0.0503 
#> 6 UniFrac     Female - Male           -0.00105 != 0  0.963   0.963      -0.00419
    
    biom <- subset(biom, `Body Site` %in% c('Saliva', 'Stool', 'Buccal mucosa'))
    bdiv_stats(biom, stat.by = "Body Site", split.by = "==Sex")[,1:6]
#> # Model:    gam(.distance ~ `Body Site`, method = "REML")
#> # A tibble: 30 × 6
#>    Sex    `Body Site`                         .mean.diff .h1     .p.val   .adj.p
#>    <fct>  <chr>                                    <dbl> <fct>    <dbl>    <dbl>
#>  1 Female Buccal mucosa - Buccal mucosa vs S…     -0.791 != 0  1.66e-35 2.91e-34
#>  2 Female Buccal mucosa - Saliva vs Stool         -0.789 != 0  1.94e-35 2.91e-34
#>  3 Female Buccal mucosa - Buccal mucosa vs S…     -0.596 != 0  5.30e-23 5.30e-22
#>  4 Male   Saliva - Saliva vs Stool                -0.528 != 0  4.09e-22 3.06e-21
#>  5 Male   Saliva - Buccal mucosa vs Stool         -0.526 != 0  5.30e-22 3.18e-21
#>  6 Female Buccal mucosa vs Saliva - Buccal m…     -0.195 != 0  2.44e-20 1.22e-19
#>  7 Female Buccal mucosa vs Saliva - Saliva v…     -0.193 != 0  3.80e-20 1.63e-19
#>  8 Female Saliva - Buccal mucosa vs Stool         -0.505 != 0  5.18e-20 1.94e-19
#>  9 Female Saliva - Saliva vs Stool                -0.503 != 0  5.95e-20 1.98e-19
#> 10 Male   Buccal mucosa - Saliva vs Stool         -0.531 != 0  9.76e-19 2.93e-18
#> # ℹ 20 more rows

Test beta diversity for associations with metadata.

Usage

Arguments

Value

See also

Examples