correlatedBgEffects: Simulate correlated background effects.

Description Usage Arguments Details Value See Also Examples

View source: R/variancecomponentFunctions.R

Description

correlatedBgEffects computes a background effect that simulates structured correlation between the phenotypes.

Usage

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correlatedBgEffects(N, P, pcorr = NULL, corr_mat = NULL, sampleID = "ID_",
  phenoID = "Trait_", id_samples = NULL, id_phenos = NULL,
  verbose = FALSE)

Arguments

N

Number [integer] of samples to simulate.

P

Number [integer] of phenotypes to simulate.

pcorr

Initial strength of correlation [double] between neighbouring traits. Decreases by pcorr^(distance); distance from 0 to P-1. See details.

corr_mat

[P x P] correlation matrix [double] as covariance component for the multivariate normal distribution. If not provided, pcorr is used to construct the correlation matrix.

sampleID

Prefix [string] for naming samples.

phenoID

Prefix [string] for naming traits.

id_samples

Vector of [NrSamples] sample IDs [string]; if not provided constructed by paste(sampleID, 1:N, sep="").

id_phenos

Vector of [NrTraits] phenotype IDs [string]; if not provided constructed by paste(phenoID, 1:P, sep="").

verbose

[boolean] If TRUE, progress info is printed to standard out.

Details

correlatedBgEffects can be used to simulate phenotypes with a defined level of correlation between traits. If the corr_mat is not provided, a simple correlation structure based on the distance of the traits will be constructed. Traits of distance d=1 (adjacent columns) will have correlation cor=pcorr^1, traits with d=2 have cor=pcorr^2 up to traits with d=(P-1) cor=pcorr^{(P-1)} and 0 < pcorr < 1. The correlated background effect correlated is simulated based on this correlation structure C: correlated ~ N_{NP}(0,C).

Value

Named list with [N x P] matrix of correlated background effects ( correlatedBg) and the correlation matrix (cov_correlated). If corr_mat provided corr_mat == cov_correlated.

See Also

rmvnorm which is used to simulate the multivariate normal distribution

Examples

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correlatedBg <- correlatedBgEffects(N=100, P=20, pcorr=0.4)

PhenotypeSimulator documentation built on May 14, 2018, 1:04 a.m.