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Simulation of trajectories from two samples diverging by a delta function

simul_data.Rd

Simulate n_obs1 trajectories of length n_point in the first sample and n_obs2 trajectories of length n_point in the second sample.

Usage

simul_data(
  n_point,
  n_obs1,
  n_obs2,
  c_val = 0,
  delta_shape = "constant",
  distrib = "normal",
  max_iter = 10000
)

Arguments

n_point

integer value, number of points in the trajectory.

n_obs1

integer value, number of trajectories in the first sample.

n_obs2

integer value, number of trajectories in the second sample.

c_val

numeric value, level of divergence between the two samples.

delta_shape

character string, shape of the divergence between the two samples, among "constant", "linear", "quadratic".

distrib

character string, type of probability distribution used to simulate the data among "normal", "cauchy", "dexp", "student".

max_iter

integer, maximum number of iteration for the iterative simulation process.

Value

A list with the following elements

  • mat_sample1: numeric matrix of dimension n_point x n_obs1 containing n_obs1 trajectories (in columns) of size n_point (in rows) corresponding to sample 1.

  • mat_sample2: numeric matrix of dimension n_point x n_obs2 containing n_obs2 trajectories (in columns) of size n_point (in rows) corresponding to sample 2.

References

Zaineb Smida, Lionel Cucala, Ali Gannoun & Ghislain Durif (2022) A median test for functional data, Journal of Nonparametric Statistics, 34:2, 520-553, doi:10.1080/10485252.2022.2064997 , hal-03658578

See also

plot_simu(), simul_traj()

Examples

simu_data <- simul_data(
    n_point = 100, n_obs1 = 50, n_obs2 = 75, c_val = 10, 
    delta_shape = "constant", distrib = "normal"
)
str(simu_data)
#> List of 5
#>  $ mat_sample1: num [1:100, 1:50] 10 10.1 10.1 10.1 10.2 ...
#>  $ mat_sample2: num [1:100, 1:75] 0 -0.0297 -0.059 -0.0874 -0.1148 ...
#>  $ c_val      : num 10
#>  $ distrib    : chr "normal"
#>  $ delta_shape: chr "constant"