generate_data: Generate realistic datasets

In StevenGolovkine/simulater: Simulate functional data based on real datasets

Generate realistic datasets

Description

This function generates realistic irregularly sampled functional dataset given mean and covariance functions.

Usage

generate_data(
  n,
  m,
  model_mean,
  covariance,
  model_noise,
  lambda,
  ti = NULL,
  grid = seq(0, 1, length.out = 101),
  p = 0.2,
  k = 1
)

Arguments

n	Number of curves to generate.
m	Mean number of observation points per curve.
model_mean	glmnet model for the mean curve.
covariance	Matrix for the covariance surface.
model_noise	Object of class 'gam' from the function learn_noise.
lambda	Value of the penalty parameter for the mean curve.
ti	Sampling points of each curves, default=NULL.
grid	Common grid for the curves, default=seq(0, 1, length.out = 101).
p	Uncertainty for the number of observation per curve, default=0.2.
k	Multiplicative factor for the noise variance, default=1.

Details

The data are generated as

X = μ + Σ u + ε,

where μ is the mean function, Σ is the square-root of the covariance matrix, u and ε are random normal variables. Heteroscedasticity is allowed using the coefs parameter.

Value

List containing n entries. Each of the entry represents a simulated curve as another list with three entries:

• $t the sampling points.

• $x the observed points.

• $x_true the observed points without noise.

See Also

map,map2 Uniform,Normal mvrnorm

Examples

## Not run: 
if(interactive()){
 attach(powerconsumption)
 mod <- learn_mean(df = powerconsumption, k = 50)
 cov <- learn_covariance(powerconsumption, 'lm')
 coefs <- learn_noise(df = powerconsumption)
 df <- generate_data(n = 10, m = 40, model_mean = mod, covariance = cov,
                     model_noise = coefs, lambda = exp(-3.5),
                     ti = NULL, grid = seq(0, 1, length.out = 101),
                     p = 0.2, k = 1)
 }

## End(Not run)

StevenGolovkine/simulater documentation built on April 4, 2022, 5:04 a.m.