sim {relSim}

Perform the relatives simulation

Description

Generate N pairs with a given relationship and calculate the LR for sibs, parent-child and the number of matching alleles

Usage

sim(
  N,
  Freqs,
  rel = "UN",
  save = FALSE,
  strPath = "",
  strVer = "",
  BlockSize = N/100,
  fileName = NULL
)

Arguments

N

The number of iterations to carry out

Freqs

A list containing two lists labelled loci and freqs. The second list is a list of vectors containing the allele frequencies of each allele at each locus in the multiplex.

rel

generate unrelated (rel = 'UN'), full-sibs (rel = 'FS'), or parent child (rel = 'PC') pairs

save

Write the results to disk if TRUE

strPath

Optional prefix to add to the results file path so that the output location can be specified

strVer

Optional suffix for the results file. This is useful when running multiple instances of R

BlockSize

Sets the number of random profiles to be generated in each iteration. By default the block size is set to 1 percent of the total sample size. It is unclear whether the procedure is more efficient if a bigger percentage of the total is used. Users must take care to make sure that the block size evenly divides N otherwise the procedure will exit

fileName

This argument lets the user override the default result file naming scheme

Details

This is the function that generates all the data for the results in the paper. WARNING: this function is not especially fast. To achieve the 100 million iterations used in the paper, 30 instances of R were launched on a multicore server. Each instance represented one relationship with 10 million iterations. The compute time for this arrangement was approximately 1 hours, meaning a full serial run would have taken over 30 hours to achieve the same result.

Value

a data frame with three columns: sib, pc, ibs containing the LRs for full-siblings, parent-child, and the number of matching alleles for each generated pair of profiles.

Author(s)

James M. Curran

See Also

readResults, errorRate

Examples

## not run
## this replicates Ge et al.'s experiment and takes about 45 minutes
## to run (I think)
## Not run: 
data(fbiCaucs)
N = 1000000
sim(N, fbiCaucs, save = T)
sim(N, fbiCaucs, 'FS', save = T)
sim(N, fbiCaucs, 'PC', save = T)

## End(Not run)