ses.UniFrac | R Documentation |
Standardized effect size of unifrac
ses.UniFrac( physeq, method = "taxa.labels", fixedmar = "both", shuffle = "both", strata = NULL, mtype = "count", burnin = 0, thin = 1, weighted = TRUE, normalized = TRUE, runs = 99, cores = 1 )
physeq |
phyloseq-class, containing at minimum a phylogenetic tree and otu table |
method |
"taxa.labels" shuffles labels in phylogenetic tree (Ignores fixedmar, shuffle, strata, mtype). If NULL then no swap algorithm is applied (i.e. uses permatfull from vegan). Else the method used for the swap algorithm ("swap", "tswap", "quasiswap", "backtrack"). If mtype="count" the "quasiswap", "swap", "swsh" and "abuswap" methods are available (see details). |
fixedmar |
Character, stating which of the row/column sums should be preserved ("none", "rows", "columns", "both"). |
shuffle |
Character, indicating whether individuals ("ind"), samples ("samp") or both ("both") should be shuffled. |
strata |
Numeric vector or factor for grouping samples within strata for restricted permutations. Unique values or levels are used. |
mtype |
Matrix data type, either "count" for count data, or "prab" for presence-absence type incidence data. |
burnin |
Number of null communities discarded before proper analysis in sequential ("swap", "tswap") methods. |
thin |
Number of discarded permuted matrices between two evaluations in sequential ("swap", "tswap") methods. |
weighted |
Should unifrac be weighted by species abundance? (default = TRUE) |
normalized |
(Optional). Logical. Should the output be normalized such that values range from 0 to 1 independent of branch length values? Default is TRUE. Note that (unweighted) UniFrac is always normalized by total branch-length, and so this value is ignored when weighted == FALSE. |
runs |
Number of randomizations |
cores |
Number of cores to use for UniFrac of null communities. Default is 1 |
See permat (vegan) for detailed options on permutation
A list of results:
unifrac.obs - Observed unifrac between communities
unifrac.rand.mean - Mean unifrac between null communities
unifrac.rand.sd - Standard deviation of unifrac between null communities
unifrac.obs.rank - Rank of observed unifrac vs. null unifrac
unifrac.obs.z - Standardized effect size of unifrac vs. null unifrac (= (unifrac.obs - unifrac.rand.mean) / unifrac.rand.sd)
unifrac.obs.p - P-value (quantile) of observed unifrac vs. null communities (= unifrac.obs.rank / runs + 1)
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