Published values used by the tests and vignettes

Reads inst/extdata/reference.csv: one row per published number, with its source, the R call that recomputes it with this package (call), the expression that extracts the number from the result (extract, in terms of x), the printed value and the digits to compare. The same file drives the test of published values and the vignette that reproduces the published examples. Each distinct call is evaluated once.

Usage

bam_reference(source = NULL, evaluate = FALSE, level = 1)

Arguments

source

optional regular expression; keep rows whose source matches.

evaluate

logical; evaluate the rows and add columns computed and agrees (default FALSE).

level

largest level evaluated (1 = fast rows, 2 = slow rows; default 1); rows above it get NA.

Value

A data.table with columns id, source, quantity, call, extract, value, digits, level, note (and computed, agrees).

See also

Other bayesammi utilities: bam_notation(), bam_save(), bam_theme()

Author

Muhammad Yaseen (myaseen208@gmail.com)

Examples

ref <-
  bam_reference(
      source   = NULL
    , evaluate = FALSE
    , level    = 1
    )
ref
#>                       id                                         source
#>                   <char>                                         <char>
#> 1: crossa2011_ls_lambda1 Crossa et al. (2011), Crop Science 51, p. 1463
#> 2: crossa2011_ls_lambda2 Crossa et al. (2011), Crop Science 51, p. 1463
#> 3:       crossa2011_mean Crossa et al. (2011), Crop Science 51, Table 1
#>                                              quantity
#>                                                <char>
#> 1:           least-squares lambda_1 of the maize data
#> 2:           least-squares lambda_2 of the maize data
#> 3: grand mean of the maize data (posterior mean 4858)
#>                                                                    call
#>                                                                  <char>
#> 1: ge_ammi(.data = Maiz, .y = y, .gen = entry, .env = site, .rep = rep)
#> 2: ge_ammi(.data = Maiz, .y = y, .gen = entry, .env = site, .rep = rep)
#> 3:             ge_mean(.data = Maiz, .y = y, .gen = entry, .env = site)
#>         extract value digits level   note
#>          <char> <int>  <int> <int> <char>
#> 1: x$lambdas[1]  5923      0     1       
#> 2: x$lambdas[2]  3070      0     1       
#> 3: x$grand_mean  4858      0     1       
# \donttest{
bam_reference(
    source   = "."
  , evaluate = TRUE
  , level    = 1
  )
#>                       id                                         source
#>                   <char>                                         <char>
#> 1: crossa2011_ls_lambda1 Crossa et al. (2011), Crop Science 51, p. 1463
#> 2: crossa2011_ls_lambda2 Crossa et al. (2011), Crop Science 51, p. 1463
#> 3:       crossa2011_mean Crossa et al. (2011), Crop Science 51, Table 1
#>                                              quantity
#>                                                <char>
#> 1:           least-squares lambda_1 of the maize data
#> 2:           least-squares lambda_2 of the maize data
#> 3: grand mean of the maize data (posterior mean 4858)
#>                                                                    call
#>                                                                  <char>
#> 1: ge_ammi(.data = Maiz, .y = y, .gen = entry, .env = site, .rep = rep)
#> 2: ge_ammi(.data = Maiz, .y = y, .gen = entry, .env = site, .rep = rep)
#> 3:             ge_mean(.data = Maiz, .y = y, .gen = entry, .env = site)
#>         extract value digits level   note computed agrees
#>          <char> <int>  <int> <int> <char>    <num> <lgcl>
#> 1: x$lambdas[1]  5923      0     1        5922.891   TRUE
#> 2: x$lambdas[2]  3070      0     1        3070.454   TRUE
#> 3: x$grand_mean  4858      0     1        4858.138   TRUE
# }