noiseFixedEffects simulates a number of non-genetic covariate effects (confounders). Confounders can have effects across all traits (shared) or to a number of traits only (independent); in addition, only a certain proportion of traits can be affected by the confounders. Confounders can be simulated as categorical variables or following a binomial , uniform or normal distribution. Effect sizes for the noise effects can be simulated from a uniform or normal distribution. Multiple confounder sets drawn from different distributions/different parameters of the same distribution can be simulated by specifying NrFixedEffects and supplying the respective distribution parameters.
noiseFixedEffects(N, P, NrConfounders = 10, sampleID = "ID_", phenoID = "Trait_", id_samples = NULL, id_phenos = NULL, pTraitsAffected = 1, NrFixedEffects = 1, pIndependentConfounders = 0.4, pTraitIndependentConfounders = 0.2, keepSameIndependent = FALSE, distConfounders = "norm", mConfounders = 0, sdConfounders = 1, catConfounders = NULL, probConfounders = NULL, distBeta = "norm", mBeta = 0, sdBeta = 1, verbose = FALSE)
| N | Number [integer] of samples to simulate. |
|---|---|
| P | Number [integer] of phenotypes to simulate. |
| NrConfounders | Vector of number(s) [integer] of confounders from a specified distribution to simulate. |
| 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=""). |
| pTraitsAffected | Vector of proportion(s) [double] of traits affected by the confounders. For non-integer results of pTraitsAffected*P, the ceiling of the result is used. |
| NrFixedEffects | Number [integer] of different confounder effects to simulate; allows to simulate fixed effects from different distributions or with different parameters; if only one type of confounder distribution is wanted, set NrFixedEffects=1 and choose the number of confounders with eg NrConfounders=10. |
| pIndependentConfounders | Vector of proportion(s) [double] of confounders to have a trait-independent effect. |
| pTraitIndependentConfounders | Vector of proportion(s) [double] of traits influenced by independent confounder effects. |
| keepSameIndependent | [boolean] If set to TRUE, the independent genetic effects always influence the same subset of traits. |
| distConfounders | Vector of name(s) [string] of distribution to use to simulate confounders; one of "unif", "norm", "bin", "cat_norm", "cat_unif". |
| mConfounders | Vector of mean/midpoint(s) [double] of normal/uniform distribution for confounders. |
| sdConfounders | Vector of standard deviation(s)/distance from midpoint(s) [double] of normal/uniform distribution for confounders. |
| catConfounders | Vector of number(s) of confounder categories [integer]; required if distConfounders "cat_norm" or "cat_unif". |
| probConfounders | Vector of probability(s) [double] of binomial confounders (0/1); required if distConfounders "bin". |
| distBeta | Vector of name(s) [string] of distribution to use to simulate effect sizes of confounders; one of "unif" or "norm". |
| mBeta | Vector of mean/midpoint [double] of normal/uniform distribution for effect sizes of confounders. |
| sdBeta | Vector of standard deviation/distance from midpoint [double] of normal/uniform distribution for effect sizes of confounders. |
| verbose | [boolean] If TRUE, progress info is printed to standard out |
Named list of shared confounder effects (shared: [N x P] matrix), independent confoudner effects (independent: [N x P] matrix), the confounders labeled as shared or independent effect (cov: [NrConfounders x N] matrix) and the simulated effect sizes of the confounders (cov_effect: [P x NrConfounders] dataframe).
# fixed noise effect with default setting noiseFE <- noiseFixedEffects(P=5, N=20) # 1 categorical fixed noise effect with uniform distribution of the # categories noiseFE_catUnif <- noiseFixedEffects(P=10, N=20, NrConfounders=1, distConfounders="cat_unif", catConfounders=3) # 10 fixed noise effect with uniform distribution between 1 and 5 (3 +/- 2) # categories noiseFE_uniformConfounders_normBetas <- noiseFixedEffects(P=10, N=20, NrConfounders=10, distConfounders="unif", mConfounders=3, sdConfounders=2, distBeta="norm", sdBeta=2) # 4 fixed noise effect with binomial distribution with p=0.2 noiseFE_binomialConfounders_uniformBetas <- noiseFixedEffects(P=10, N=20, NrConfounders=4, distConfounders="bin", probConfounders=0.2, distBeta="norm", sdBeta=2) # 2 fixed noise effect with 1 binomial confounders and 1 normally # distributed confounder; the latter only affects 2 traits noiseFE_binomialandNormalConfounders <- noiseFixedEffects(P=10, N=20, NrFixedEffects=2, pTraitsAffected =c (1,0.2), NrConfounders=c(2,2), distConfounders=c("bin", "norm"), probConfounders=0.2)