mc_sdwdnsp2: Simulate several data sets with different non-selectivity...

mc_sdwdnsp2R Documentation

Simulate several data sets with different non-selectivity profiles defined by setting 2 using parallel computation

Description

Performance; 100 - TT 2 sec TF 2 sec FT 0.25 sec FF 0.15 sec, 1,000 - TT 3.9 sec TF 2.3 sec FT 2.1 sec FF 1.7 sec, 10,000 TT 8.1 sec TF 6 sec FT 18 sec FF 17.5 sec

Usage

mc_sdwdnsp2(
  parameter_row = c(1),
  m = 100,
  n = "r",
  R = "r",
  cv_vec = "r",
  ci = "r",
  mmax = 2.5,
  q = c(0, 0.25),
  parallel = TRUE,
  progress_bar = TRUE
)

Arguments

parameter_row

A data frame or data table with only 1 row. Should contain a subset of simulation parameters. Parameters that may be included are n, R, cvx, cvy, ci_lwr, ci_upr, mmax, qmin and qmax

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

mmax

A number signifying the maximum multiple of relocation of clinical samples affected by differences in selectivity

q

A vector with two elements signifying the lower and upper quantile boundaries where clinical samples are relocated

parallel

should we allow parallelization to be performed

progress_bar

should a progress bar be displayed. Default is TRUE

Details

parallel should only be TRUE used if parameter_row has few rows, or else it will be faster to just use standard sapply() by setting parallel to FALSE. This function is not available for other users than Windows.

Value

A list of simulated data tables with 4 columns, where two of which are measurement results from two MSs in comparison having differences in selectivity profiles defined by setting 2. In addition, n * R rows

Examples

mc_sdwdnsp2(m = 50)

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