mc_sdwinsp: Simulate several data sets with identical non-selectivity...

In pernille267/commutability.selectivity: Commutability and non-selectivity evaluation tools

Simulate several data sets with identical non-selectivity profiles using parallel computation

Description

Simulate several data sets with identical non-selectivity profiles using parallel computation

Usage

mc_sdwinsp(
  parameter_row = c(2),
  m = 100,
  n = "r",
  R = "r",
  cv_vec = "r",
  ci = "r",
  parallel = FALSE,
  progress_bar = TRUE
)

Arguments

parameter_row	A data frame or data table with only 1 row. Should contain a subset of simulation parameters. Possible simulation parameters are n, R, cvx, cvy, ci_lwr and ci_upr
m	Integer - The number of data sets to be simulated
n	Integer or 'r' - number of unique samples
R	Integer or 'r' - Number of unique replicated measurements on each sample
cv_vec	Vector with two elements or 'r' - MS CVs in decimal
ci	Vector with two elements or 'r' - Concentration interval
parallel	Should we allow parallel computing to be performed when simulating from sdwinsp(). This is not recommended if the number of parameter combinations is larger than 10. Default value is FALSE
progress_bar	Should a progress bar monitoring the simulation progress be displayed. Default is TRUE

Details

this function is recommended to use when simulating more than 1000 data sets, or else it will be faster to just use standard sapply

Value

A list containing m data tables with 4 columns, two of which are measured results from two MSs in comparison. In addition, n * R rows.

Examples

mc_sdwinsp(m = 50)

pernille267/commutability.selectivity documentation built on July 20, 2022, 9:11 a.m.