Estimation statistics for numeric variable correlation on a phyloseq object

Computes diversity metrics (Hill numbers by default) per sample and assesses their relationship with a numeric variable using bootstrap confidence intervals for correlation coefficients and regression slopes.

Usage

estim_cor_pq(
  physeq,
  variable,
  hill_scales = c(0, 1, 2),
  custom_fn = NULL,
  method = "pearson",
  resamples = 5000,
  ci = 95,
  na_remove = TRUE
)

Arguments

physeq: (phyloseq, required) A phyloseq object.
variable: (character, required) The name of a numeric column in sample_data.
hill_scales: (numeric vector, default c(0, 1, 2)) The q values for Hill number computation.
custom_fn: (function, default NULL) An optional custom diversity function (see estim_diff_pq() for details).
method: (character, default "pearson") Correlation method. One of "pearson", "spearman", "kendall".
resamples: (integer, default 5000) Number of bootstrap resamples.
ci: (numeric, default 95) Confidence interval level (0-100).
na_remove: (logical, default TRUE) If TRUE, samples with NA in variable are removed.

Value

A list of class "estim_cor_pq_result" with components:

data: The diversity data.frame used for analysis
correlations: A tibble with columns: metric, estimate, ci_lower, ci_upper, method, pvalue
regressions: A tibble with columns: metric, intercept, slope, slope_ci_lower, slope_ci_upper
plots: A named list of ggplot2 scatter plots with regression line and bootstrap CI ribbon (one per metric)

Details

Examples

if (FALSE) { # \dontrun{
library(phyloseq)
data("data_fungi", package = "MiscMetabar")

# Add a numeric variable for demonstration
sam <- sample_data(data_fungi)
sam$lib_size <- sample_sums(data_fungi)
sample_data(data_fungi) <- sam

res <- estim_cor_pq(data_fungi, variable = "lib_size")
res
res$plots$Hill_0
res$correlations
} # }