Simulate a complete training dataset, which may be representative of various applications. Several flexible arguments allow adjustment of the number of individuals, of observed inputs, and the values of many parameters controlling the data generation.

## Usage

simu_db(
M = 10,
N = 10,
K = 1,
covariate = FALSE,
grid = seq(0, 10, 0.05),
grid_cov = seq(0, 10, 0.5),
common_input = TRUE,
common_hp = TRUE,
int_mu_v = c(4, 5),
int_mu_l = c(0, 1),
int_i_v = c(1, 2),
int_i_l = c(0, 1),
int_i_sigma = c(0, 0.2),
lambda_int = c(30, 40),
m_int = c(0, 10),
lengthscale_int = c(30, 40),
m0_slope = c(-5, 5),
m0_intercept = c(-50, 50)
)

## Arguments

M

An integer. The number of individual per cluster.

N

An integer. The number of observations per individual.

K

An integer. The number of underlying clusters.

covariate

A logical value indicating whether the dataset should include an additional input covariate named 'Covariate'.

grid

A vector of numbers defining a grid of observations (i.e. the reference inputs).

grid_cov

A vector of numbers defining a grid of observations (i.e. the covariate reference inputs).

common_input

A logical value indicating whether the reference inputs are common to all individual.

common_hp

A logical value indicating whether the hyper-parameters are common to all individual. If TRUE and K>1, the hyper-parameters remain different between the clusters.

A logical value indicating whether the values of hyper-parameters should be added as columns in the dataset.

A logical value indicating whether the name of the clusters should be added as a column in the dataset.

int_mu_v

A vector of 2 numbers, defining an interval of admissible values for the variance hyper-parameter of the mean process' kernel.

int_mu_l

A vector of 2 numbers, defining an interval of admissible values for the lengthscale hyper-parameter of the mean process' kernel.

int_i_v

A vector of 2 numbers, defining an interval of admissible values for the variance hyper-parameter of the individual process' kernel.

int_i_l

A vector of 2 numbers, defining an interval of admissible values for the lengthscale hyper-parameter of the individual process' kernel.

int_i_sigma

A vector of 2 numbers, defining an interval of admissible values for the noise hyper-parameter.

lambda_int

A vector of 2 numbers, defining an interval of admissible values for the lambda parameter of the 2D exponential.

m_int

A vector of 2 numbers, defining an interval of admissible values for the mean of the 2D exponential.

lengthscale_int

A vector of 2 numbers, defining an interval of admissible values for the lengthscale parameter of the 2D exponential.

m0_slope

A vector of 2 numbers, defining an interval of admissible values for the slope of m0.

m0_intercept

A vector of 2 numbers, defining an interval of admissible values for the intercept of m0.

## Value

A full dataset of simulated training data.

## Examples

## Generate a dataset with 3 clusters of 4 individuals, observed at 10 inputs
data = simu_db(M = 4, N = 10, K = 3)

## Generate a 2-D dataset with an additional input 'Covariate'
data = simu_db(covariate = TRUE)

## Generate a dataset where input locations are different among individuals
data = simu_db(common_input = FALSE)

## Generate a dataset with an additional column indicating the true clusters
data = simu_db(K = 3, add_clust = TRUE)