Generate an artificial longitudinal data set.
simulate_data( N, t_data, covariates = c(), names = NULL, relevances = c(1, 1, rep(1, length(covariates))), n_categs = rep(2, sum(covariates %in% c(2, 3))), t_jitter = 0, lengthscales = rep(12, 2 + sum(covariates %in% c(0, 1, 2))), f_var = 1, noise_type = "gaussian", snr = 3, phi = 1, gamma = 0.2, N_affected = round(N/2), t_effect_range = "auto", t_observed = "after_0", c_hat = 0, dis_fun = "gp_warp_vm", bin_kernel = FALSE, steepness = 0.5, vm_params = c(0.025, 1), continuous_info = list(mu = c(pi/8, pi, 0.5), lambda = c(pi/8, pi, 1)), N_trials = 1, force_zeromean = TRUE )
N  Number of individuals. 

t_data  Measurement times (same for each individual, unless

covariates  Integer vector that defines the types of covariates (other than id and age). If not given, only the id and age covariates are created. Different integers correspond to the following covariate types:

names  Covariate names. 
relevances  Relative relevance of each component. Must have be a vector
so that 
n_categs  An integer vector defining the number of categories
for each categorical covariate, so that 
t_jitter  Standard deviation of the jitter added to the given measurement times. 
lengthscales  A vector so that 
f_var  variance of f 
noise_type  Either "gaussian", "poisson", "nb" (negative binomial), "binomial", or "bb" (betabinomial). 
snr  The desired signaltonoise ratio. This argument is valid
only when 
phi  The inverse overdispersion parameter for negative binomial data.
The variance is 
gamma  The dispersion parameter for betabinomial data. 
N_affected  Number of diseased individuals that are affected by the
disease. This defaults to the number of diseased individuals. This argument
can only be given if 
t_effect_range  Time interval from which the disease effect times are sampled uniformly. Alternatively, This can any function that returns the (possibly randomly generated) real disease effect time for one individual. 
t_observed  Determines how the disease effect time is observed. This
can be any function that takes the real disease effect time as an argument
and returns the (possibly randomly generated) observed onset/initiation time.
Alternatively, this can be a string of the form 
c_hat  a constant added to f 
dis_fun  A function or a string that defines the disease effect. If
this is a function, that function is used to generate the effect.
If 
bin_kernel  Should the binary kernel be used for categorical
covariates? If this is 
steepness  Steepness of the input warping function. This is only used if the disease component is in the model. 
vm_params  Parameters of the variance mask function. This is only
needed if 
continuous_info  Info for generating continuous covariates. Must be a
list containing fields

N_trials  The number of trials parameter for binomial data. 
force_zeromean  Should each component (excluding the disease age component) be forced to have a zero mean? 
An object of class lgpsim.