predict_ionize: Predicting trends in ionization efficiency

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

Predicting trends in ionization efficiency

Description

Fluctuations in electronics, development of the sputter pit geometry and the analysed substrate can all cause trends and fluctuations in the secondary ion current. This function attempts to accommodate the global trend in the ionization trend by application of a GAM model. The nested variant can then be used to gauge whether a set of analyses are likely to originate from a homogeneous substrate at the level of individual analysis.

Usage

predict_ionize(
  .IC,
  ...,
  .nest = NULL,
  .X = NULL,
  .N = NULL,
  .species = NULL,
  .t = NULL,
  .bl_t = NULL,
  .plot = TRUE,
  .method = "median",
  .hide = TRUE
)

Arguments

.IC	A tibble containing raw ion count data.
...	Variables for grouping.
.nest	A variable identifying a groups of analyses which indicates whether a nested mixed GAM model is applied.
.X	A variable constituting the ion count rate.
.N	A variable constituting the ion counts.
.species	A variable constituting the species analysed.
.t	A variable constituting the time increments.
.bl_t	A variable constituting the blanking time.
.plot	Logical indicating whether to plot ion trends
.method	Method for calculating de-trended single ion counts
.hide	A logical indicating whether only processed data should be returned. If TRUE The model parameters are contained as an attribute named "modeldata".

Examples

# remove zero count analysis
tb_0 <- zeroCt(real_IC, "12C", "40Ca 16O", sample.nm, file.nm, .warn = FALSE)

# predict ionization trends
## Not run: predict_ionize(tb_0, file.nm)

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