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Forward selection of explanatory variables

Source: R/community-tests.R
ord_select.Rd

Selects site variables for RDA (or CCA, db-RDA) by forward selection with the double stopping criterion of Blanchet et al. (2008): a variable enters only if it is significant and the adjusted \(R^2\) of the model stays below that of the full model. Uses vegan::ordiR2step().

Usage

ord_select(
  data,
  site = site,
  species = species,
  abundance = NULL,
  formula,
  env = NULL,
  method = c("rda", "cca", "dbrda"),
  transform = NULL,
  distance = "bray",
  alpha = 0.05,
  permutations = 999
)

Arguments

data

A data frame in long format, with one row per record.

site, species

<tidy-select> The column holding site identifiers and the column holding species names.

abundance

<tidy-select> The column holding counts. If NULL (the default), a column called abundance is used when present, otherwise every row counts as one individual.

formula

One-sided formula of all candidate site variables.

env

A data frame of site variables with a column named like the site column. By default, site variables are taken from data: every column that is constant within each site.

method

"rda", "cca" or "dbrda".

transform

Transformation of the community matrix before the analysis: "hellinger" (the default for PCA and RDA, which otherwise overweight abundant species; Legendre and Gallagher 2001), "chord", "log" (log(x) + 1 for x > 0), "sqrt", "pa" (presence-absence) or "none" (the default for the other methods).

distance

Dissimilarity for "pcoa", "nmds" and "dbrda", passed to vegan::vegdist(), such as "bray" (the default) or "jaccard".

alpha

Significance level for entry.

permutations

Number of permutations for the tests of passive variables.

Value

A tibble with one row per selected step: step, term, adj_r2 (cumulative), statistic and p.value, and the adjusted \(R^2\) of the full model as attribute "adj_r2_full".

References

Blanchet, F. G., Legendre, P. and Borcard, D. (2008). Forward selection of explanatory variables. Ecology, 89, 2623-2632. doi:10.1890/07-0986.1

Examples

ants <- dplyr::mutate(png_ants, unit = paste(site, season, transect))
set.seed(1)
ord_select(ants, site = unit, formula = ~ elevation + season,
           permutations = 199)
#> # A tibble: 1 x 5
#>    step term      adj_r2 statistic p.value
#>   <int> <chr>      <dbl>     <dbl>   <dbl>
#> 1     1 elevation  0.101      3.13   0.005