stats_table: Run non-parametric statistics on a data.frame.

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

Run non-parametric statistics on a data.frame.

Description

A simple interface to lower-level statistics functions, including stats::wilcox.test(), stats::kruskal.test(), emmeans::emmeans(), and emmeans::emtrends().

Usage

stats_table(
  df,
  regr = NULL,
  resp = attr(df, "response"),
  stat.by = NULL,
  split.by = NULL,
  test = "emmeans",
  fit = "gam",
  at = NULL,
  level = 0.95,
  alt = "!=",
  mu = 0,
  p.adj = "fdr"
)

Arguments

df	The dataset (data.frame or tibble object). "Dataset fields" mentioned below should match column names in df. Required.
regr	Dataset field with the x-axis (independent; predictive) values. Must be numeric. Default: NULL
resp	Dataset field with the y-axis (dependent; response) values, such as taxa abundance or alpha diversity. Default: attr(df, 'response')
stat.by	Dataset field with the statistical groups. Must be categorical. Default: NULL
split.by	Dataset field(s) that the data should be split by prior to any calculations. Must be categorical. Default: NULL
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 stats_tables: adiv_stats(), bdiv_stats(), distmat_stats(), taxa_stats()

Examples

    library(rbiom)
    
    biom <- rarefy(hmp50)
    
    df <- taxa_table(biom, rank = "Family")
    stats_table(df, stat.by = "Body Site")[,1:6]
    
    df <- adiv_table(biom)
    stats_table(df, stat.by = "Sex", split.by = "Body Site")[,1:7]

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