adiv_corrplot: Visualize alpha diversity with scatterplots and trendlines.

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

Visualize alpha diversity with scatterplots and trendlines.

Description

Visualize alpha diversity with scatterplots and trendlines.

Usage

adiv_corrplot(
  biom,
  x,
  adiv = "Shannon",
  layers = "tc",
  stat.by = NULL,
  facet.by = NULL,
  colors = TRUE,
  shapes = TRUE,
  test = "emmeans",
  fit = "gam",
  at = NULL,
  level = 0.95,
  p.adj = "fdr",
  transform = "none",
  alt = "!=",
  mu = 0,
  caption = TRUE,
  check = FALSE,
  ...
)

Arguments

biom	An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.
x	Dataset field with the x-axis values. Equivalent to the regr argument in stats_table(). Required.
adiv	Alpha diversity metric(s) to use. Options are: "OTUs", "Shannon", "Chao1", "Simpson", and/or "InvSimpson". Set adiv=".all" to use all metrics. Multiple/abbreviated values allowed. Default: "Shannon"
layers	One or more of c("trend", "confidence", "point", "name", "residual"). Single letter abbreviations are also accepted. For instance, c("trend", "point") is equivalent to c("t", "p") and "tp". Default: "tc"
stat.by	Dataset field with the statistical groups. Must be categorical. Default: NULL
facet.by	Dataset field(s) to use for faceting. Must be categorical. Default: NULL
colors	How to color the groups. Options are: TRUE - Automatically select colorblind-friendly colors. FALSE or NULL - Don't use colors. a palette name - Auto-select colors from this set. E.g. "okabe" character vector - Custom colors to use. E.g. c("red", "#00FF00") named character vector - Explicit mapping. E.g. c(Male = "blue", Female = "red") See "Aesthetics" section below for additional information. Default: TRUE
shapes	Shapes for each group. Options are similar to colors's: TRUE, FALSE, NULL, shape names (typically integers 0 - 17), or a named vector mapping groups to specific shape names. See "Aesthetics" section below for additional information. Default: TRUE
test	Method for computing p-values: 'none', '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
p.adj	Method to use for multiple comparisons adjustment of p-values. Run p.adjust.methods for a list of available options. Default: "fdr"
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"
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
caption	Add methodology caption beneath the plot. Default: TRUE
check	Generate additional plots to aid in assessing data normality. Default: FALSE
...	Additional parameters to pass along to ggplot2 functions. Prefix a parameter name with a layer name to pass it to only that layer. For instance, p.size = 2 ensures only the points have their size set to 2.

Value

A ggplot2 plot. The computed data points, ggplot2 command, stats table, and stats table commands are available as ⁠$data⁠, ⁠$code⁠, ⁠$stats⁠, and ⁠$stats$code⁠, respectively.

Aesthetics

All built-in color palettes are colorblind-friendly. The available categorical palette names are: "okabe", "carto", "r4", "polychrome", "tol", "bright", "light", "muted", "vibrant", "tableau", "classic", "alphabet", "tableau20", "kelly", and "fishy".

Shapes can be given as per base R - numbers 0 through 17 for various shapes, or the decimal value of an ascii character, e.g. a-z = 65:90; A-Z = 97:122 to use letters instead of shapes on the plot. Character strings may used as well.

See Also

Other alpha_diversity: adiv_boxplot(), adiv_stats(), adiv_table()

Other visualization: adiv_boxplot(), bdiv_boxplot(), bdiv_corrplot(), bdiv_heatmap(), bdiv_ord_plot(), plot_heatmap(), rare_corrplot(), rare_multiplot(), rare_stacked(), stats_boxplot(), stats_corrplot(), taxa_boxplot(), taxa_corrplot(), taxa_heatmap(), taxa_stacked()

Examples

    library(rbiom)
    
    p <- adiv_corrplot(babies, "age", stat.by = "deliv", fit = "gam")
    
    p
    
    p$stats
    
    p$code

rbiom documentation built on April 3, 2025, 6:39 p.m.