View source: R/beta_div_test.R
| var_par_pq | R Documentation | 
The function partitions the variation in otu_table using
distance (Bray per default) with respect to two, three, or four explanatory
tables, using
adjusted R² in redundancy analysis ordination (RDA) or distance-based
redundancy analysis. If response is a single vector, partitioning is by
partial regression. Collinear variables in the explanatory tables do NOT
have to be removed prior to partitioning. See vegan::varpart() for more
information.
var_par_pq(
  physeq,
  list_component,
  dist_method = "bray",
  dbrda_computation = TRUE
)
physeq | 
 (required): a   | 
list_component | 
 (required) A named list of 2, 3 or four vectors with
names from the   | 
dist_method | 
 (default "bray") the distance used. See
  | 
dbrda_computation | 
 (logical) Do dbrda computations are runned for each individual component (each name of the list component) ?  | 
This function is mainly a wrapper of the work of others.
Please make a reference to vegan::varpart() if you
use this function.
an object of class "varpart", see vegan::varpart()
Adrien Taudière
var_par_rarperm_pq(), vegan::varpart(), plot_var_part_pq()
if (requireNamespace("vegan")) {
  data_fungi_woNA <-
    subset_samples(data_fungi, !is.na(Time) & !is.na(Height))
  res_var <- var_par_pq(data_fungi_woNA,
    list_component = list(
      "Time" = c("Time"),
      "Size" = c("Height", "Diameter")
    ),
    dbrda_computation = TRUE
  )
}
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