rbiom (version 2.1.2)

distmat_stats: Run statistics on a distance matrix vs a categorical or numeric variable.

Description

Run statistics on a distance matrix vs a categorical or numeric variable.

Usage

distmat_stats(dm, groups, test = "adonis2", seed = 0, permutations = 999)

Value

A data.frame with summary statistics from vegan::permustats(). The columns are:

.n -

The size of the distance matrix.

.stat -

The observed statistic. For mrpp, this is the overall weighted mean of group mean distances.

.z -

The difference of observed statistic and mean of permutations divided by the standard deviation of permutations (also known as z-values). Evaluated from permuted values without observed statistic.

.p.val -

Probability calculated by test.

R commands for reproducing the results are in $code.

Arguments

dm

A dist-class distance matrix, as returned from bdiv_distmat() or stats::dist(). Required.

groups

A named vector of grouping values. The names should correspond to attr(dm, 'Labels'). Values can be either categorical or numeric. Required.

test

Permutational test for accessing significance. Options are:

"adonis2" -

Permutational MANOVA; vegan::adonis2().

"mrpp" -

Multiple response permutation procedure; vegan::mrpp().

"none" -

Don't run any statistics.

Abbreviations are allowed. Default: "adonis2"

seed

Random seed for permutations. Must be a non-negative integer. Default: 0

permutations

Number of random permutations to use. Default: 999

See Also

Other beta_diversity: bdiv_boxplot(), bdiv_clusters(), bdiv_corrplot(), bdiv_heatmap(), bdiv_ord_plot(), bdiv_ord_table(), bdiv_stats(), bdiv_table()

Other stats_tables: adiv_stats(), bdiv_stats(), stats_table(), taxa_stats()

Examples

    library(rbiom)
    
    hmp10        <- hmp50$clone()
    hmp10$counts <- hmp10$counts[,1:10]
    
    dm <- bdiv_distmat(hmp10, 'unifrac')
    
    distmat_stats(dm, groups = pull(hmp10, 'Body Site'))
    
    distmat_stats(dm, groups = pull(hmp10, 'Age'))
    
    # See the R code used to calculate these statistics:
    stats <- distmat_stats(dm, groups = pull(hmp10, 'Age'))
    stats$code