calc_gSGC_feldspar: Calculate Global Standardised Growth Curve (gSGC) for...

In R-Lum/Luminescence: Comprehensive Luminescence Dating Data Analysis

Calculate Global Standardised Growth Curve (gSGC) for Feldspar MET-pIRIR

Description

Implementation of the gSGC approach for feldspar MET-pIRIR by Li et al. (2015)

Usage

calc_gSGC_feldspar(
  data,
  gSGC.type = "50LxTx",
  gSGC.parameters,
  n.MC = 100,
  plot = FALSE
)

Arguments

data	data.frame (required): data frame with five columns per sample c("LnTn", "LnTn.error", "Lr1Tr1", "Lr1Tr1.error","Dr1")
gSGC.type	character (with default): growth curve type to be selected according to Table 3 in Li et al. (2015). Allowed options are "50LxTx", "50Lx", "50Tx", "100LxTx", "100Lx", "100Tx", "150LxTx", "150Lx", "150Tx", "200LxTx", "200Lx", "200Tx", "250LxTx", "250Lx", "250Tx"
gSGC.parameters	data.frame (optional): an own parameter set for the gSGC with the following columns y1, y1_err, D1 D1_err, y2, y2_err, y0, y0_err.
n.MC	numeric (with default): number of Monte-Carlo runs for the error calculation
plot	logical (with default): enable/disable the plot output.

Details

##TODO

Value

Returns an S4 object of type RLum.Results.

⁠@data⁠
⁠$ df⁠ (data.frame)
.. $DE the calculated equivalent dose
.. $DE.ERROR error on the equivalent dose, which is the standard deviation of the MC runs
.. $HPD95_LOWER lower boundary of the highest probability density (95%)
.. $HPD95_UPPER upper boundary of the highest probability density (95%)
⁠$ m.MC⁠ (list) numeric vector with results from the MC runs.

⁠@info⁠
'$ call“ (call) the original function call

Function version

0.1.0

Author(s)

Harrison Gray, USGS (United States), Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)

References

Li, B., Roberts, R.G., Jacobs, Z., Li, S.-H., Guo, Y.-J., 2015. Construction of a “global standardised growth curve” (gSGC) for infrared stimulated luminescence dating of K-feldspar 27, 119–130. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.quageo.2015.02.010")}

See Also

RLum.Results, get_RLum, uniroot, calc_gSGC

Examples

##test on a generated random sample
n_samples <- 10
data <- data.frame(
  LnTn = rnorm(n=n_samples, mean=1.0, sd=0.02),
  LnTn.error = rnorm(n=n_samples, mean=0.05, sd=0.002),
  Lr1Tr1 = rnorm(n=n_samples, mean=1.0, sd=0.02),
  Lr1Tr1.error = rnorm(n=n_samples, mean=0.05, sd=0.002),
  Dr1 = rep(100,n_samples))

results <- calc_gSGC_feldspar(
  data = data, gSGC.type = "50LxTx",
  plot = FALSE)

plot_AbanicoPlot(results)

R-Lum/Luminescence documentation built on March 1, 2025, 12:43 a.m.