distmat_stats {rbiom} | R Documentation |
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)
Arguments
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: |
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 $code
.
See Also
Other beta_diversity:
bdiv_boxplot()
,
bdiv_clusters()
,
bdiv_corrplot()
,
bdiv_heatmap()
,
bdiv_ord_plot()
,
bdiv_ord_table()
,
bdiv_stats()
,
bdiv_table()
Other stats_tables:
adiv_stats()
,
bdiv_stats()
,
stats_table()
,
taxa_stats()
Examples
library(rbiom)
hmp10 <- hmp50$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