distmat_stats | R Documentation |
Run statistics on a distance matrix vs a categorical or numeric variable.
distmat_stats(dm, groups, test = "adonis2", seed = 0, permutations = 999)
dm |
A |
groups |
A named vector of grouping values. The names should
correspond to |
test |
Permutational test for accessing significance. Options are:
Abbreviations are allowed. Default: |
seed |
Random seed for permutations. Must be a non-negative integer.
Default: |
permutations |
Number of random permutations to use.
Default: |
A data.frame with summary statistics from vegan::permustats()
.
The columns are:
The size of the distance matrix.
The observed statistic. For mrpp, this is the overall weighted mean of group mean distances.
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.
Probability calculated by test
.
R commands for reproducing the results are in $code
.
Other beta_diversity:
bdiv_boxplot()
,
bdiv_clusters()
,
bdiv_corrplot()
,
bdiv_heatmap()
,
bdiv_ord_plot()
,
bdiv_ord_table()
,
bdiv_stats()
,
bdiv_table()
Other stats_tables:
adiv_stats()
,
bdiv_stats()
,
stats_table()
,
taxa_stats()
library(rbiom)
hmp10 <- hmp50$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
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