qtlpoly (version 0.2.4)

null_model: Null model

Description

Creates a null model (with no QTL) for each trait.

Usage

null_model(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)
# S3 method for qtlpoly.null
print(x, pheno.col = NULL, ...)
# S3 method for qtlpoly.null
print(x, pheno.col = NULL, ...)

Value

An object of class qtlpoly.null which contains a list of results for each trait with the following components:

pheno.col

a phenotype column number.

stat

a vector containing values from score statistics.

pval

a vector containing p-values from score statistics.

qtls

a data frame with information from the mapped QTL (NULL at this point).

Arguments

data

an object of class qtlpoly.data.

offset.data

a data frame with the same dimensions of data$pheno containing offset variables; if NULL (default), no offset variables are considered.

pheno.col

a numeric vector with the phenotype columns to be analyzed; if NULL, all phenotypes from 'data' will be included.

n.clusters

number of parallel processes to spawn.

plot

a suffix for the file's name containing simple plots of every QTL search round, e.g. "null" (default); if NULL, no file is produced.

verbose

if TRUE (default), current progress is shown; if FALSE, no output is produced.

x

an object of class qtlpoly.null to be printed.

...

currently ignored

Author

Guilherme da Silva Pereira,

References

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. tools:::Rd_expr_doi("10.1534/genetics.120.303080").

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

See Also

read_data

Examples

  # \donttest{
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null models
  null.mod = null_model(data = data, pheno.col = 1, n.clusters = 1)
  # }