DGP: Data Generating Process

In AllelicSeries: Allelic Series Test

Data Generating Process

Description

Generate a data set consisting of:

• anno: (snps x 1) annotation vector.

• covar: (subjects x 6) covariate matrix.

• geno: (subjects x snps) genotype matrix.

• pheno: (subjects x 1) phenotype vector.

• type: Either "binary" or "quantitative".

Usage

DGP(
  anno = NULL,
  beta = c(1, 2, 3),
  binary = FALSE,
  geno = NULL,
  include_residual = TRUE,
  indicator = FALSE,
  maf_range = c(0.001, 0.005),
  method = "none",
  n = 100,
  prop_anno = c(0.5, 0.4, 0.1),
  prop_causal = 1,
  random_signs = FALSE,
  random_var = 0,
  snps = 100,
  weights = c(1, 1, 1)
)

Arguments

anno	Annotation vector, if providing genotypes. Should match the number of columns in geno.
beta	If method = "none", a (L x 1) coefficient with effect sizes for each annotation category. By default, there are L = 3 annotation categories corresponding to BMVs, DMVs, and PTVs. If method != "none", a scalar effect size for the allelic series burden score.
binary	Generate binary phenotype? Default: FALSE.
geno	Genotype matrix, if providing genotypes.
include_residual	Include residual? If FALSE, returns the expected value. Intended for testing.
indicator	Convert raw counts to indicators? Default: FALSE.
maf_range	Range of minor allele frequencies: c(MIN, MAX).
method	Genotype aggregation method. Default: "none".
n	Sample size.
prop_anno	Proportions of annotations in each category. Length should equal the number of annotation categories. Default of c(0.5, 0.4, 0.1) is based on the approximate empirical frequencies of BMVs, DMVs, and PTVs.
prop_causal	Proportion of variants which are causal. Default: 1.0.
random_signs	Randomize signs? FALSE for burden-type genetic architecture, TRUE for SKAT-type.
random_var	Frailty variance in the case of random signs. Default: 0.
snps	Number of SNP in the gene. Default: 100.
weights	Annotation category weights. Length should match prop_anno.

Value

List containing: genotypes, annotations, covariates, phenotypes.

Examples

# Generate data.
data <- DGP(n = 100)

# View components.
table(data$anno)
head(data$covar)
head(data$geno[, 1:5])
hist(data$pheno)

# Generate data with L != 3 categories.
data <- DGP(
  beta = c(1, 2, 3, 4),
  prop_anno = c(0.25, 0.25, 0.25, 0.25),
  weights = c(1, 1, 1, 1)
)

AllelicSeries documentation built on April 3, 2025, 7:46 p.m.