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Permanova (adonis) on permutations of rarefaction even depth

Source: R/beta_div_test.R
adonis_rarperm_pq.Rd

lifecycle-experimental

Permanova are computed on a given number of rarefaction with different seed.number. This reduce the risk of a random drawing of a exceptional situation of an unique rarefaction.

Usage

adonis_rarperm_pq(
  physeq,
  formula,
  dist_method = "bray",
  merge_sample_by = NULL,
  na_remove = FALSE,
  rarefy_nb_seqs = FALSE,
  verbose = TRUE,
  nperm = 99,
  progress_bar = TRUE,
  quantile_prob = 0.975,
  sample.size = min(sample_sums(physeq)),
  ...
)

Arguments

physeq

(required): a phyloseq-class object obtained using the phyloseq package.

formula

(required) the right part of a formula for vegan::adonis2(). Variables must be present in the physeq@sam_data slot.

dist_method

(default "bray") the distance used. See phyloseq::distance() for all available distances or run phyloseq::distanceMethodList(). For aitchison and robust.aitchison distance, vegan::vegdist() function is directly used.

merge_sample_by

a vector to determine which samples to merge using the merge_samples2() function. Need to be in physeq@sam_data

na_remove

(logical, default FALSE) If set to TRUE, remove samples with NA in the variables set in formula.

rarefy_nb_seqs

(logical, default FALSE) Rarefy each sample (before merging if merge_sample_by is set) using phyloseq::rarefy_even_depth(). if correction_for_sample_size is TRUE, rarefy_nb_seqs will have no effect.

verbose

(logical, default TRUE) If TRUE, prompt some messages.

nperm

(int, default = 99) The number of permutations to perform.

progress_bar

(logical, default TRUE) Do we print progress during the calculation.

quantile_prob

(float, [0:1]) the value to compute the quantile. Minimum quantile is computed using 1-quantile_prob.

sample.size

(int) A single integer value equal to the number of reads being simulated, also known as the depth. See phyloseq::rarefy_even_depth().

...

Other params to be passed on to adonis_pq() function

Value

A list of three dataframe representing the mean, the minimum quantile and the maximum quantile value for adonis results. See adonis_pq().

See also

adonis_pq()

Author

Adrien Taudière

Examples

if (requireNamespace("vegan")) {
  data_fungi_woNA <-
    subset_samples(data_fungi, !is.na(Time) & !is.na(Height))
  adonis_rarperm_pq(data_fungi_woNA, "Time*Height", na_remove = TRUE, nperm = 3)
}
#> 
  |                                                        
  |                                                  |   0%
#> `set.seed(1)` was used to initialize repeatable random subsampling.
#> Please record this for your records so others can reproduce.
#> Try `set.seed(1); .Random.seed` for the full vector
#> ...
#> 1156OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Time
#> Height
#> 
  |                                                        
  |=================                                 |  33%
#> `set.seed(2)` was used to initialize repeatable random subsampling.
#> Please record this for your records so others can reproduce.
#> Try `set.seed(2); .Random.seed` for the full vector
#> ...
#> 1160OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Time
#> Height
#> 
  |                                                        
  |=================================                 |  67%
#> `set.seed(3)` was used to initialize repeatable random subsampling.
#> Please record this for your records so others can reproduce.
#> Try `set.seed(3); .Random.seed` for the full vector
#> ...
#> 1180OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> Time
#> Height
#> 
  |                                                        
  |==================================================| 100%
#> $mean
#>           Df  SumOfSqs         R2        F     Pr(>F)
#> Model      5  2.724437 0.05067775 1.121097 0.05733333
#> Residual 105 51.040745 0.94932225       NA         NA
#> Total    110 53.765182 1.00000000       NA         NA
#> 
#> $quantile_min
#>           Df  SumOfSqs        R2        F  Pr(>F)
#> Model      5  2.633518 0.0488029 1.077451 0.03225
#> Residual 105 50.681723 0.9479599       NA      NA
#> Total    110 53.463976 1.0000000       NA      NA
#> 
#> $quantile_max
#>           Df  SumOfSqs         R2        F Pr(>F)
#> Model      5  2.782253 0.05204009 1.152836 0.0997
#> Residual 105 51.328995 0.95119710       NA     NA
#> Total    110 53.962513 1.00000000       NA     NA
#>