Simulate latent function components for longitudinal data analysis
sim.create_f( X, covariates, relevances, lengthscales, X_affected, dis_fun, bin_kernel, steepness, vm_params, force_zeromean )
input data matrix (generated by
Integer vector that defines the types of covariates (other than id and age). Different integers correspond to the following covariate types:
Relative relevance of each component. Must have be a vector
A vector so that
which individuals are affected by the disease
A function or a string that defines the disease effect. If
this is a function, that function is used to generate the effect.
Should the binary kernel be used for categorical
covariates? If this is
Steepness of the input warping function. This is only used if the disease component is in the model.
Parameters of the variance mask function. This is only
Should each component (excluding the disease age component) be forced to have a zero mean?
a data frame FFF where one column corresponds to one additive component