Permutation-based computation of p-values

Computation of the p-values associated to any statistics described in the package with the permutation methods. See Smida et al (2022) for more details.

Usage

permut_pval(MatX, MatY, n_perm = 100, stat = c("mo", "med"), verbose = FALSE)

Arguments

MatX

numeric matrix of dimension n_point x n containing n trajectories (in columns) of size n_point (in rows).

MatY

numeric matrix of dimension n_point x m containing m trajectories (in columns) of size n_point (in rows).

n_perm

integer, number of permutation to compute the p-values.

stat

character string or vector of character string, name of the statistics for which the p-values will be computed, among "mo", "med", "wmw", "hkr", "cff".

verbose

boolean, if TRUE, enable verbosity.

Value

list of named numeric value corresponding to the p-values for each statistic listed in the stat input.

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

stat_mo(), stat_med(), stat_wmw(), stat_hkr(), stat_cff(), comp_stat()

Examples

# simulate small data for the example
simu_data <- simul_data(
    n_point = 20, n_obs1 = 4, n_obs2 = 5, c_val = 10, 
    delta_shape = "constant", distrib = "normal"
)

MatX <- simu_data$mat_sample1
MatY <- simu_data$mat_sample2
res <- permut_pval(
    MatX, MatY, n_perm = 100, stat = c("mo", "med", "wmw", "hkr", "cff"), 
    verbose = TRUE)
res
#> $mo
#> [1] 0.01980198
#> 
#> $med
#> [1] 0.01980198
#> 
#> $wmw
#> [1] 0.01980198
#> 
#> $hkr
#>         T1         T2 
#> 0.02970297 0.02970297 
#> 
#> $cff
#> [1] 0.01980198
#>