sim.kernels: Compute all kernel matrices when simulating data

In jtimonen/lgpr: Longitudinal Gaussian Process Regression

Compute all kernel matrices when simulating data

Description

Compute all kernel matrices when simulating data

Usage

sim.kernels(
  X,
  types,
  lengthscales,
  X_affected,
  bin_kernel,
  useMaskedVarianceKernel,
  steepness,
  vm_params
)

Arguments

X	covariates
types	vector of covariate types, so that 1 = ID 2 = age 3 = diseaseAge 4 = other continuous covariate 5 = a categorical covariate that interacts with age 6 = a categorical covariate that acts as an offset
lengthscales	vector of lengthscales
X_affected	which individuals are affected by the disease
bin_kernel	whether or not binary (mask) kernel should be used for categorical covariates (if not, the zerosum kernel is used)
useMaskedVarianceKernel	should the masked variance kernel be used for drawing the disease component
steepness	steepness of the input warping function
vm_params	parameters of the variance mask function

Value

a 3D array

jtimonen/lgpr documentation built on Oct. 12, 2023, 11:13 p.m.