rbiom (version 2.2.0)

stats_table: 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"
)

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.

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"

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]