PhenotypeSimulator (version 0.3.4)

transformNonlinear: Phenotype transformation.

Description

Transformation of phenotype component by applying a user-specified non-linear transformation to the phenotype component.

Usage

transformNonlinear(
  component,
  alpha,
  method,
  logbase = 10,
  power = 2,
  expbase = NULL,
  transformNeg = "abs",
  f = NULL,
  verbose = TRUE
)

Arguments

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.

Value

[N x P] transformed phenotype matrix [double]

Details

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)

Examples

# NOT RUN {
# 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)
# }