QSA_test: QSA test

In MartinSchobben/point: Reading, Processing, and Analysing Raw Ion Count Data

QSA test

Description

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.

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.

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. .

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"))

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

# QSA test
QSA_test(tb_pr, "13C", "12C", file.nm)

MartinSchobben/point documentation built on May 22, 2022, 7:15 a.m.