permutations: Fixed-effect interval mapping (FEIM) model permutations
In guilherme-pereira/QTLpoly: Random-effect multiple QTL mapping in autopolyploids

Description Usage Arguments Value Author(s) References See Also Examples

Stores maximum LOD scores for a number of permutations of given phenotypes.

permutations(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.sim = 1000,
  probs = c(0.9, 0.95),
  n.clusters = NULL,
  seed = 123,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.perm'
print(x, pheno.col = NULL, probs = c(0.9, 0.95))

## S3 method for class 'qtlpoly.perm'
plot(x, pheno.col = NULL, probs = c(0.9, 0.95))

`data`	an object of class `qtlpoly.data`.
`pheno.col`	a numeric vector with the phenotype columns to be analyzed; if `NULL` (default), all phenotypes from `'data'` will be included.
`n.sim`	a number of simulations, e.g. 1000 (default).
`probs`	a vector of probability values in [0, 1] representing the quantiles, e.g. c(0.90, 0.95) for the 90% and 95% quantiles.
`n.clusters`	a number of parallel processes to spawn.
`seed`	an integer for the `set.seed()` function; if `NULL`, no reproducible seeds are set.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.perm` to be printed or plotted.

An object of class qtlpoly.perm which contains a list of results for each trait with the maximum LOD score per permutation.

LOD score thresholds for given quantiles for each trait.

A ggplot2 histogram with the distribution of ordered maximum LOD scores and thresholds for given quantiles for each trait.

Guilherme da Silva Pereira, gdasilv@ncsu.edu

Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping, Genetics 138: 963-971. http://www.genetics.org/content/138/3/963

Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB (2020) Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population, Genetics 215 (3): 579-595. http://doi.org/10.1534/genetics.120.303080.

feim

  ## Not run: 
  # load raw data
  data(maps)
  data(pheno)

  # estimate conditional probabilities using mappoly package
  library(mappoly)
  genoprob <- lapply(maps, calc_genoprob)

  # prepare data
  data <- read_data(ploidy = 6, geno.prob = genoprob, pheno = pheno, step = 1)

  # perform permutations
  perm <- permutations(data = data, n.sim = 1000, n.clusters = 4)
  
## End(Not run)