# permut_pval: Permutation-based computation of p-values In funStatTest: Statistical Testing for Functional Data

 permut_pval R Documentation

## Permutation-based computation of p-values

### Description

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.

### Author(s)

Zaineb Smida, Ghislain DURIF, Lionel Cucala

### References

Zaineb Smida, Lionel Cucala, Ali Gannoun & Ghislain Durif (2022) A median test for functional data, Journal of Nonparametric Statistics, 34:2, 520-553, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10485252.2022.2064997")}, hal-03658578

`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
``````

funStatTest documentation built on May 29, 2024, 10:26 a.m.