sim_stds: Simulate standard and sample collection

In japhir/stdsim: Simulation of standard and sample measurements for measurement optimisation for clumped isotopes

Simulate standard and sample collection

Description

This function simulates the measurement of samples and standards based on 'smpinfo' and 'stdtable' based on further arguments. It then fits a linear regression through the standards (the empirical transfer function, or ETF), which is then applied to the input sample values to calculate the final \Delta_{47}-value of the sample, using inverse regression. It can return all the intermediate steps to follow along, as well as a plot to show how the ETF is applied.

Usage

sim_stds(
  stdfreqs = c(1, 1, 1, 1, 0),
  stdn = 50,
  smpn = 50,
  stdev = 50,
  smpt = 0,
  input_slope = kslope,
  stdtable = make_std_table(slope = input_slope),
  smpinfo = make_smp_info(smpt),
  out = "none"
)

Arguments

stdfreqs	An integer/numeric vector with the proportion of each standard in stdtable.
stdn	The total number of standard measurements to simulate.
smpn	The total number of sample measurements to simulate.
stdev	The standard deviation of measurements in ppm (0.001 ‰)
smpt	The known temperature of the sample. Not needed in case smpinfo is provided.
input_slope	The input slope of the ETF.
stdtable	A tibble of standard info, generated with make_std_table().
smpinfo	A tibble of sample info, generated with make_std_table().
out	A string describing the desired output, either "none" (default), "cond", "smp", "std", "cis", "etf", "pl", or "all".

Value

Based on parameter "out", it returns either:

none	Nothing is returned, without warning.
cond	The input conditions.
smp	The full table of sample simulations.
std	The full table of standard simulations.
cis	The confidence intervals.
etf	The empirical transfer function linear regression model.
pl	The ggplot output.
all	A list of the above.

References

Brandon M. Greenwell and Christine M. Schubert Kabban (2014) investr: An R Package for Inverse Estimation. The R Journal, 6(1), 90-100. URL http://journal.r-project.org/archive/2014-1/greenwell-kabban.pdf

Examples

library(stdsim)

# set global output options
options(genplot = TRUE, verbose = TRUE)

# generate a standard reference table
eth.info <- make_std_table()

# run the simulation with the above table as input
sim_stds(stdfreqs = c(1, 1, 1, 1, 0), stdn = 80, smpn = 20, stdtable = eth.info)

# more examples
sim_stds(c(1, 1, 1, 0, 9), smpn = 20, stdn = 300, stdtable = eth.info, smpt = 10)
sim_stds(stdn = 90, smpn = 10, out = "cis", stdtable = eth.info)
sim_stds(c(1, 1, 1, 1, 0))
sim_stds(c(1, 1, 9, 1, 1), smpn = 30, stdn = 100, out = "cond")
sim_stds(c(1, 1, 9, 0, 0), smpn = 16, stdn = 84, 50, smpt = 0, out = "cis")
exmp <- sim_stds(c(1, 1, 2, 1, 2), stdtable = eth.info, out = "all")

japhir/stdsim documentation built on May 5, 2023, 8:19 a.m.