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Calculate Selection Weights

Source: R/selections.R
calc_selection_weights.Rd

Calculate selection weights for a series of recordings based on the selection parameters defined by sim_selection_weights().

Usage

calc_selection_weights(
  meta_sun,
  params,
  col_site_id = site_id,
  col_min = t2sr,
  col_day = date
)

Arguments

meta_sun

(Spatial) Data frame. Recording meta data with time to sunrise/sunset. Output of calc_sun(). Must have at least col_min, col_day, and col_site_id.

params

Named list. Parameters created by sim_selection_weights(), containing min_range, min_mean, min_sd, day_range, day_mean, day_sd, offset, return_log, selection_fun.

col_site_id

Column. Unquoted column containing site strata IDs (defaults to site_id).

col_min

Column. Unquoted column containing minutes to sunrise (t2sr) or sunset (t2ss) output from calc_sun() (defaults to t2sr).

col_day

Column. Unquoted column containing dates or day-of-year (doy) to use (defaults to date).

Value

Returns data with appended selection weights columns:

  • psel_by - The minutes column used

  • psel_min - Probability of selection by time of day (min column)

  • psel_doy - Probability of selection by day of year

  • psel - Probability of selection overall

  • psel_scaled - Probability of selection scaled overall

  • psel_std - Probability of selection standardized within a site

  • psel_normalized - Probability of selection normalized within a site

Examples

s <- clean_site_index(example_sites_clean,
  name_date_time = c("date_time_start", "date_time_end")
)
m <- clean_metadata(project_files = example_files) |>
  add_sites(s) |>
  calc_sun()
#> Extracting ARU info...
#> Extracting Dates and Times...
#> Joining by columns `date_time_start` and `date_time_end`

params <- sim_selection_weights()

calc_selection_weights(m, params = params)
#> # A tibble: 27 × 21
#>    file_name  type  path  aru_type aru_id site_id date_time           date      
#>    <chr>      <chr> <chr> <chr>    <chr>  <chr>   <dttm>              <date>    
#>  1 P01_1_202… wav   a_BA… BarLT    BARLT… P01_1   2020-05-03 05:20:00 2020-05-03
#>  2 P02_1_202… wav   a_S4… SongMet… S4A01… P02_1   2020-05-04 05:25:00 2020-05-04
#>  3 P02_1_202… wav   a_S4… SongMet… S4A01… P02_1   2020-05-05 07:30:00 2020-05-05
#>  4 P03_1_202… wav   a_BA… BarLT    BARLT… P03_1   2020-05-06 10:00:00 2020-05-06
#>  5 P06_1_202… wav   a_BA… BarLT    BARLT… P06_1   2020-05-09 05:20:00 2020-05-09
#>  6 P07_1_202… wav   a_S4… SongMet… S4A01… P07_1   2020-05-09 05:25:00 2020-05-09
#>  7 P07_1_202… wav   a_S4… SongMet… S4A01… P07_1   2020-05-10 07:30:00 2020-05-10
#>  8 P08_1_202… wav   a_BA… BarLT    BARLT… P08_1   2020-05-11 10:00:00 2020-05-11
#>  9 P09_1_202… wav   a_S4… SongMet… S4A02… P09_1   2020-05-11 05:00:00 2020-05-11
#> 10 P01_1_202… wav   j_BA… BarLT    BARLT… P01_1   2020-05-03 05:20:00 2020-05-03
#> # ℹ 17 more rows
#> # ℹ 13 more variables: longitude <dbl>, latitude <dbl>, tz <chr>, t2sr <dbl>,
#> #   t2ss <dbl>, doy <dbl>, psel_by <chr>, psel_min <dbl>, psel_doy <dbl>,
#> #   psel <dbl>, psel_scaled <dbl>, psel_std <dbl>, psel_normalized <dbl>