Tidy and model-ready output

Every tidydiversity result is already a tidy tibble with one row per site and order q. The generics::tidy() methods rename the columns to the broom conventions (estimate, std.error, conf.low, conf.high) and add an inverse-variance weight (weight, 1 / se^2, or NA when the standard error is zero, as at a site with a single species), so results stack with other broom output and can go straight into weighted models. generics::glance() gives one row per site with its sample size, observed richness and coverage.

add_site_data() joins site-level covariates, such as elevation, habitat or treatment, from the original data or a separate site table, so diversity can be modelled against them.

Usage

# S3 method for class 'td_estimate'
tidy(x, ...)

# S3 method for class 'td_standardise'
tidy(x, ...)

# S3 method for class 'td_rarefy'
tidy(x, ...)

# S3 method for class 'td_boot'
tidy(x, ...)

# S3 method for class 'td_beta_pair'
tidy(x, ...)

# S3 method for class 'td_coverage'
glance(x, ...)

add_site_data(x, data, site = site, cols = NULL)

Arguments

x

A tidydiversity result.

...

Not used.

data

A data frame holding the site column and the covariates, such as the community data the result came from, or a site table.

site

<tidy-select> The site column of data, matched to the site column of x.

cols

<tidy-select> Covariates to join. By default every column that is constant within each site and not already in x.

Value

A tibble.

Examples

set.seed(1)
est <- div_estimate(png_ants, q = 0, nboot = 20)
generics::tidy(est)
#> # A tibble: 5 x 8
#>   site       q observed estimate std.error conf.low conf.high  weight
#>   <chr>  <dbl>    <dbl>    <dbl>     <dbl>    <dbl>     <dbl>   <dbl>
#> 1 MW0200     0       30     44.0      9.06    34.4       74.6 0.0122 
#> 2 MW0700     0       35     67       30.8     41.5      192.  0.00105
#> 3 MW1200     0       26     36.0     14.2     27.3      104.  0.00497
#> 4 MW1700     0       10     11.5      1.94    10.2       20.5 0.265  
#> 5 MW2200     0        9     11.2      4.34     9.18      35.1 0.0531 

# Join elevation and fit a weighted model
est <- add_site_data(est, png_ants)
fit <- lm(estimate ~ elevation,
  data = generics::tidy(est),
  weights = weight
)
coef(fit)
#> (Intercept)   elevation 
#> 42.26075138 -0.01699226