Description Usage Arguments Value See Also Examples

View source: R/variancecomponentFunctions.R

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
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).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
# 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)
``` |

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