QSA test

QSA_test function to test for QSA.

Usage

QSA_test(
  .IC,
  .ion1,
  .ion2,
  ...,
  .nest = NULL,
  .X = NULL,
  .N = NULL,
  .species = NULL,
  .t = NULL,
  .plot = TRUE
)

Arguments

.IC

A tibble containing processed ion count data.

.ion1

A character string constituting the heavy isotope ("13C").

.ion2

A character string constituting the light isotope ("12C").

...

Variables for grouping.

.nest

A variable hat identifies a series of analyses to calculate the significance of QSA.

.X

A variable constituting the ion count rate (defaults to variables generated with read_IC().)

.N

A variable constituting the ion counts (defaults to variables generated with read_IC().).

.species

A variable constituting the species analysed (defaults to variables generated with read_IC().).

.t

A variable constituting the time of the analyses (defaults to variables generated with read_IC().).

.plot

Currently not supported.

Value

A tibble::tibble() containing the original dataset and adds the variables: beta, t_QSA, and p_QSA that summarise the results of an linear model fitted by OLS (respectively; the slope and the associated student's t test statistic and p value) on the ion count rates of the common isotope (as predictor) and the isotope ratio (as dependent variable). The p value is for beta being different from zero. .

Details

The accuracy of pulsed ion counting is influenced by systematic errors which depend on the ion counting system. Quasi simultaneous arrival is one of those potential errors that can also impact isotope ratios.

Examples

# Use point_example() to access the examples bundled with this package

# raw data containing 13C and 12C counts on carbonate
tb_rw <- read_IC(point_example("2018-01-19-GLENDON"))
#> Registered S3 methods overwritten by 'readr':
#>   method                    from 
#>   as.data.frame.spec_tbl_df vroom
#>   as_tibble.spec_tbl_df     vroom
#>   format.col_spec           vroom
#>   print.col_spec            vroom
#>   print.collector           vroom
#>   print.date_names          vroom
#>   print.locale              vroom
#>   str.col_spec              vroom

# Processing raw ion count data
tb_pr <- cor_IC(tb_rw)

# QSA test
QSA_test(tb_pr, "13C", "12C", file.nm)
#> # A tibble: 11,700 × 12
#>    file.nm    t.nm Xt.pr.12C Xt.pr.13C N.pr.12C N.pr.13C R_Xt.pr alpha_Xt.pr.12C
#>    <chr>     <dbl>     <dbl>     <dbl>    <dbl>    <dbl>   <dbl>           <dbl>
#>  1 2018-01-…  0.54    34460.      358.    12040      125 0.0104           0.0114
#>  2 2018-01-…  1.08    34202.      341.    11950      119 0.00996          0.0114
#>  3 2018-01-…  1.62    34632.      395.    12100      138 0.0114           0.0114
#>  4 2018-01-…  2.16    34191.      366.    11946      128 0.0107           0.0114
#>  5 2018-01-…  2.7     34855.      378.    12178      132 0.0108           0.0114
#>  6 2018-01-…  3.24    34672.      369.    12114      129 0.0106           0.0114
#>  7 2018-01-…  3.78    34766.      369.    12147      129 0.0106           0.0114
#>  8 2018-01-…  4.32    34609.      366.    12092      128 0.0106           0.0114
#>  9 2018-01-…  4.86    34414.      406.    12024      142 0.0118           0.0114
#> 10 2018-01-…  5.4     34474.      409.    12045      143 0.0119           0.0114
#> # … with 11,690 more rows, and 4 more variables: beta_Xt.pr.12C <dbl>,
#> #   t_Xt.pr.12C <dbl>, p_Xt.pr.12C <dbl>, delta_Xt.pr.12C <dbl>