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Test multiple times effect of factor on Hill diversity with different rarefaction even depth

Source: R/alpha_div_test.R
hill_test_rarperm_pq.Rd

lifecycle-experimental

This reduce the risk of a random drawing of a exceptional situation of an unique rarefaction.

Usage

hill_test_rarperm_pq(
  physeq,
  fact,
  q = c(0, 1, 2),
  nperm = 99,
  sample.size = min(sample_sums(physeq)),
  verbose = FALSE,
  progress_bar = TRUE,
  p_val_signif = 0.05,
  type = "non-parametrique",
  ...
)

Arguments

physeq

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

fact

(required) Name of the factor in physeq@sam_data used to plot different lines

q

(a vector of integer) The list of q values to compute the hill number H^q. If Null, no hill number are computed. Default value compute the Hill number 0 (Species richness), the Hill number 1 (exponential of Shannon Index) and the Hill number 2 (inverse of Simpson Index). Hill numbers are more appropriate in DNA metabarcoding studies when q > 0 (Alberdi & Gilbert, 2019; Calderón-Sanou et al., 2019).

nperm

(int) The number of permutations to perform.

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().

verbose

(logical). If TRUE, print additional information.

progress_bar

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

p_val_signif

(float, [0:1]) The mimimum value of p-value to count a test as significant int the prop_signif result.

type

A character specifying the type of statistical approach (See ggstatsplot::ggbetweenstats() for more details):

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

...

Additional arguments passed on to ggstatsplot::ggbetweenstats() function

Value

A list of 6 components :

  • method

  • expressions

  • plots

  • pvals

  • prop_signif

  • statistics

References

Alberdi, A., & Gilbert, M. T. P. (2019). A guide to the application of Hill numbers to DNA-based diversity analyses. Molecular Ecology Resources. doi:10.1111/1755-0998.13014

Calderón-Sanou, I., Münkemüller, T., Boyer, F., Zinger, L., & Thuiller, W. (2019). From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices? Journal of Biogeography, 47. doi:10.1111/jbi.13681

See also

ggstatsplot::ggbetweenstats(), hill_pq()

Author

Adrien Taudière

Examples

# \donttest{
if (requireNamespace("ggstatsplot")) {
  hill_test_rarperm_pq(data_fungi, "Time", nperm = 2)
  res <- hill_test_rarperm_pq(data_fungi, "Height", nperm = 9, p.val = 0.9)
  patchwork::wrap_plots(res$plots[[1]])
  res$plots[[1]][[1]] + res$plots[[2]][[1]] + res$plots[[3]][[1]]
  res$prop_signif
  res_para <- hill_test_rarperm_pq(data_fungi, "Height", nperm = 9, type = "parametrique")
  res_para$plots[[1]][[1]] + res_para$plots[[2]][[1]] + res_para$plots[[3]][[1]]
  res_para$pvals
  res_para$method
  res_para$expressions[[1]]
}
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#> list(italic("F")["Welch"](2, 84.35) == "0.10", italic(p) == "0.90", 
#>     widehat(omega["p"]^2) == "0.00", CI["95%"] ~ "[" * "0.00", 
#>     "1.00" * "]", italic("n")["obs"] == "131")
# }