Description Usage Arguments Details Value See Also Examples
View source: R/variancecomponentFunctions.R
correlatedBgEffects computes a background effect that simulates structured correlation between the phenotypes.
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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. |
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).
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.
rmvnorm
which is used to simulate the
multivariate normal distribution
1 | correlatedBg <- correlatedBgEffects(N=100, P=20, pcorr=0.4)
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