Set simulation model.

Based on parameters provided, this function sets the name for the phenotype simulation. It carries out compatibiltiy checks of the specifie parameters and checks for any missing information.

setModel(genVar = NULL, h2s = NULL, theta = 0.8, h2bg = NULL,
  eta = 0.8, noiseVar = NULL, delta = NULL, gamma = 0.8,
  rho = NULL, phi = NULL, alpha = 0.8, pcorr = 0.6,
  pIndependentConfounders = 0.4, pTraitIndependentConfounders = 0.2,
  pIndependentGenetic = 0.4, pTraitIndependentGenetic = 0.2,
  proportionNonlinear = 0, cNrSNP = NULL, NrConfounders = 10,
  verbose = TRUE)

Arguments

genVar	Total genetic variance [double].
h2s	Proportion [double] of variance of genetic variant effects.
theta	Proportion [double] of variance of shared genetic variant effects.
h2bg	Proportion [double] of variance of infinitesimal genetic effects i.e. correlation introduced by sample kinship).
eta	Proportion [double] of variance of shared infinitesimal genetic effects.
noiseVar	Total noise variance [double].
delta	Proportion [double] of variance of non-genetic covariate effect.
gamma	Proportion [double] of variance of shared non-genetic covariate effects.
rho	Proportion [double] of variance of correlated noise effects.
phi	Proportion [double] of variance of observational noise effects.
alpha	Proportion [double] of variance of shared observational noise effect.
pcorr	Correlation [double] between phenotypes.
pIndependentConfounders	Proportion [double] of non-genetic covariate to have a trait-independent effect.
pTraitIndependentConfounders	Proportion [double] of traits influenced by independent non-genetic covariate effects.
pIndependentGenetic	Proportion [double] of genetic variant effects to have a trait-independent fixed effect.
pTraitIndependentGenetic	Proportion [double] of traits influenced by independent genetic variant effects.
proportionNonlinear	[double] proportion of the phenotype to be non- linear
cNrSNP	Number [integer] of causal SNPs; used as genetic variant effects.
NrConfounders	Number [integer] of non-genetic covariates; used as non-genetic covariate effects.
verbose	[boolean]; If TRUE, progress info is printed to standard out.

Value

Named list containing the genetic model (modelGenetic), the noise model (modelNoise) and the input parameters (h2s, h2bg, noiseVar, rho, delta, phi, gamma, theta, eta, alpha, pcorr, proportionNonlinear). Model options are: modelNoise: "noNoise", "noiseFixedOnly", "noiseBgOnly", "noiseCorrelatedOnly", "noiseFixedAndBg","noiseCorrelatedAndBg", "noiseFixedAndCorrelated", "noiseFixedAndBgAndCorrelated" modelGenetic: "noGenetic","geneticBgOnly", "geneticFixedOnly", "geneticFixedAndBg"

Examples

#genetic fixed effects only
model <- setModel(genVar=1, h2s=1)
#> The total noise variance (noiseVar) is: 0
#> The noise model is: noNoise
#> The total genetic variance (genVar) is: 1
#> The genetic model is: geneticFixedOnly
#> Proportion of variance of genetic variant effects (h2s): 1
#> Proportion of variance of shared genetic variant effects (theta): 0.8
#> Proportion of genetic variant effects to have a trait-independent fixed effect (pIndependentGenetic): 0.4
#> Proportion of traits influenced by independent genetic variant effects (pTraitIndependentGenetic): 0.2
#> Proportion of non-linear phenotype transformation (proportionNonlinear): 0
#> 

#genetic fixed and bg effects
model <- setModel(genVar=1, h2s=0.01)
#> The total noise variance (noiseVar) is: 0
#> The noise model is: noNoise
#> The total genetic variance (genVar) is: 1
#> The genetic model is: geneticFixedAndBg
#> Proportion of variance of genetic variant effects (h2s): 0.01
#> Proportion of variance of shared genetic variant effects (theta): 0.8
#> Proportion of genetic variant effects to have a trait-independent fixed effect (pIndependentGenetic): 0.4
#> Proportion of traits influenced by independent genetic variant effects (pTraitIndependentGenetic): 0.2
#> Proportion of variance of infinitesimal genetic effects (h2bg): 0.99
#> Proportion of variance of shared infinitesimal genetic effects (eta): 0.8
#> Proportion of non-linear phenotype transformation (proportionNonlinear): 0
#> 

#genetic and noise fixed effects only
model <- setModel(genVar=0.4, h2s=1, delta=1)
#> The total noise variance (noiseVar) is: 0.6
#> The noise model is: noiseFixedOnly
#> Proportion of non-genetic covariate variance (delta): 1
#> Proportion of variance of shared non-genetic covariate effects (gamma): 0.8
#> Proportion of non-genetic covariates to have a trait-independent effect (pIndependentConfounders ): 0.4
#> Proportion of traits influenced by independent non-genetic covariate effects (pTraitIndependentConfounders): 0.2
#> 
#> The total genetic variance (genVar) is: 0.4
#> The genetic model is: geneticFixedOnly
#> Proportion of variance of genetic variant effects (h2s): 1
#> Proportion of variance of shared genetic variant effects (theta): 0.8
#> Proportion of genetic variant effects to have a trait-independent fixed effect (pIndependentGenetic): 0.4
#> Proportion of traits influenced by independent genetic variant effects (pTraitIndependentGenetic): 0.2
#> Proportion of non-linear phenotype transformation (proportionNonlinear): 0
#>