adiv_corrplot: Visualize alpha diversity with scatterplots and trendlines.

In cmmr/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 = "lm",
  at = NULL,
  level = 0.95,
  p.adj = "fdr",
  trans = "none",
  alt = "!=",
  mu = 0,
  caption = TRUE,
  ...
)

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. Default: "Shannon" Multiple/abbreviated values allowed.
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: 'lm'
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"
trans	Transformation to apply. Options are: c("none", "rank", "log", "log1p", "sqrt"). "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
...	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

cmmr/rbiom documentation built on April 28, 2024, 6:38 a.m.