generate.sample.outcomes.matrix: Returns a matrix of outcome samples, either permutations or...

In gkeele/miqtl: Suite of QTL Mapping Functions

Returns a matrix of outcome samples, either permutations or from the null model of no locus effect

Description

This function takes an scan.h2lmm() object, and returns a specified number of outcome samples, either permutations or from the null model of no locus effect.

Usage

generate.sample.outcomes.matrix(
  scan.object,
  model.type = c("null", "alt"),
  method = c("bootstrap", "permutation", "subsample"),
  subsample.prop = 0.63,
  subsample.chr = NULL,
  use.REML = TRUE,
  use.BLUP = FALSE,
  num.samples,
  seed = 1
)

Arguments

scan.object	A scan.h2lmm() object.
model.type	DEFAULT: "null". "null" specifies sampling processes from the null model. "alt" specifies sampling processes from the alternative model.
method	DEFAULT: "bootstrap". "bootstrap" specifies parametric bootstraps from the given model. "permutation" specifies parametric permutations that can respect the structure of the data. Permutations are more appropriate if the data have highly influential data points. "subsample" specifies randomly sampling without replacement some proportion of the data. This is done by placing NAs in the observations not selected.
subsample.prop	DEFAULT: 0.63. The proportion of the original data set to sample.
subsample.chr	DEFAULT: NULL. If method "subsample" is specified, no locus need be specified. If NULL, the default behavior is to grab the locus (and chromosome) with the peak association. If chromosome is specified, its peak locus will be grabbed to be passed to the scanning procedure.
use.REML	DEFAULT: TRUE. Determines whether the variance components for the parametric sampling are based on maximizing the likelihood (ML) or the residual likelihood (REML).
use.BLUP	DEFAULT: FALSE.This results in the BLUP value of the polgyene effect (assuming a GRM has been given) is used, rather than sampled. This reduces the variation seen across sampling, which can result in narrower positional confidence intervals.
num.samples	The number of parametric bootstrap samples to return.
seed	DEFAULT: 1. The sampling process is random, thus a seed must be set for samples to be consistent across machines.

Examples

generate.sample.outcomes.matrix()

gkeele/miqtl documentation built on Sept. 13, 2024, 5:49 a.m.