Transformation of phenotype component by applying a user-specified non-linear transformation to the phenotype component.
transformNonlinear(component, alpha, method, logbase = 10, power = 2, expbase = NULL, transformNeg = "abs", f = NULL, verbose = TRUE)
| component | [N x P] Phenotype matrix [double] where [N] are the number of samples and P the number of phenotypes |
|---|---|
| alpha | [double] weighting scalar for non-linearity: alpha==0 fully linear phenotype, alpha==1 fully non-linear phenotype. See @details. |
| method | [string] one of exp (exponential), log (logarithm), poly (polynomial), sqrt (squareroot) or custom (user-supplied function) |
| logbase | [int] when method==log, sets the log base for transformation |
| power | [double] when method==poly, sets the power to raise to. |
| expbase | [double] when method==exp, sets the exp base for transformation. |
| transformNeg | [string] one of abs (absolute value) or set0 (set all negative values to zero). If method==log and transformNeg==set0, negative values set to 1e-5 |
| f | [function] function accepting component as a single argument. |
| verbose | [boolean]; If TRUE, progress info is printed to standard out. |
[N x P] transformed phenotype matrix [double]
transformNonlinear takes a phenotype component as input and transforms it according to the specified transformation method. The user can choose how strongly non-linear the resulting phenotype component should be, by specifying the weighting parameter alpha: component_transformed = (1 - alpha) \* component + alpha \* transformfunction(component)
# Simulate non-genetic covariate effects cov_effects <- noiseFixedEffects(N=100, P=5) # Transform logarithmically covs_log <- transformNonlinear(cov_effects$shared, alpha=0.5, method="log", transformNeg="abs")#># Transform custom f_custom <- function(x) {x^2 + 3*x} covs_custom <- transformNonlinear(cov_effects$shared, alpha=0.5, method="custom", f=f_custom)#>