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Classify statistical significance

Source: R/cpr_classify_signif.R
cpr_classify_signif.Rd

Given the results of cpr_rand_test(), classifies statistical significance of a biodiversity metric. The null hypothesis is that observed value does not lie in the extreme of the random values.

Usage

cpr_classify_signif(df, metric, one_sided = FALSE, upper = FALSE)

Arguments

df

Input data frame.

metric

Character vector of length 1; selected metric to classify significance. May choose from pd (phylogenetic diversity), rpd (relative phylogenetic diversity), pe (phylogenetic endemism), rpe (relative phylogenetic endemism) (case-sensitive).

one_sided

Logical vector of length 1; is the null hypothesis one-sided? If TRUE, values will be classified as significant if they are in either the top 5% or bottom 5%. If FALSE, values will be classified as significant if they are in the top 2.5% or bottom 2.5%, combined.

upper

Logical vector of length 1; only applies if one_sided is TRUE. If TRUE, values in the top 5% will be classified as significant. If FALSE, values in the bottom 5% will be classified as significant.

Value

Object of class data.frame with column added for statistical significance of the selected metric. The new column name is the name of the metric with _signif appended. The new column is a character that may contain the following values, depending on the null hypothesis:

  • < 0.01, < 0.025, > 0.975, > 0.99, not significant (two-sided)

  • < 0.01, < 0.05, > 0.99, > 0.95, not significant (one-sided)

Details

For metrics like pe, you probably want to consider a one-sided hypothesis testing values in the upper extreme (i.e., we are interested in areas that have higher than expected endemism). For this, you would set one_sided = TRUE, upper = TRUE. For metrics like pd, you probably want to consider a two-sided hypothesis (i.e., we are interested in areas that are either more diverse or less than diverse than expected at random). For this, set one_sided = FALSE.

Examples

# \donttest{
set.seed(12345)
data(phylocom)
rand_test <- cpr_rand_test(
  phylocom$comm, phylocom$phy,
  null_model = "curveball", metrics = "pd", n_reps = 50
)
#> Warning: Abundance data detected. Results will be the same as if using presence/absence data (no abundance weighting is used).
#> Warning: Dropping tips from the tree because they are not present in the community data: 
#>  sp16, sp23, sp27, sp28, sp30, sp31, sp32
cpr_classify_signif(rand_test, "pd")
#>            pd_obs pd_rand_mean pd_rand_sd  pd_obs_z pd_obs_c_upper
#> clump1  0.3018868    0.4649057 0.03446764 -4.729621              0
#> clump2a 0.3207547    0.4664151 0.03831667 -3.801488              0
#> clump2b 0.3396226    0.4686792 0.03542398 -3.643198              0
#> clump4  0.4150943    0.4645283 0.03364201 -1.469412              2
#> even    0.5660377    0.4656604 0.02987904  3.359457             50
#> random  0.5094340    0.4716981 0.03073249  1.227881             44
#>         pd_obs_c_lower pd_obs_q pd_obs_p_upper pd_obs_p_lower       pd_signif
#> clump1              50       50           0.00           1.00          < 0.01
#> clump2a             50       50           0.00           1.00          < 0.01
#> clump2b             50       50           0.00           1.00          < 0.01
#> clump4              44       50           0.04           0.88 not significant
#> even                 0       50           1.00           0.00          > 0.99
#> random               2       50           0.88           0.04 not significant
# }