power2 {nevada}

Power Simulations for Permutation Tests

Description

This function provides a Monte-Carlo estimate of the power of the permutation tests proposed in this package.

Usage

power2(
  model1 = "gnp",
  model2 = "k_regular",
  n1 = 20L,
  n2 = 20L,
  num_vertices = 25L,
  model1_params = NULL,
  model2_params = NULL,
  representation = "adjacency",
  distance = "frobenius",
  stats = c("flipr:t_ip", "flipr:f_ip"),
  B = 1000L,
  alpha = 0.05,
  test = "exact",
  k = 5L,
  R = 1000L,
  seed = 1234
)

Arguments

model1

A string specifying the model to be used for generating the first sample. Choices are "sbm", "k_regular", "gnp", "smallworld", "pa", "poisson" and "binomial". Defaults to "gnp".

model2

A string specifying the model to be used for generating the second sample. Choices are "sbm", "k_regular", "gnp", "smallworld", "pa", "poisson" and "binomial". Defaults to "k_regular".

n1

The size of the first sample. Defaults to 20L.

n2

The size of the second sample. Defaults to 20L.

num_vertices

The number of nodes in the generated graphs. Defaults to 25L.

model1_params

A named list setting the parameters of the first chosen model. Defaults to list(p = 1/3).

model2_params

A named list setting the parameters of the second chosen model. Defaults to list(k = 8L).

representation

A string specifying the desired type of representation, among: "adjacency", "laplacian" and "modularity". Defaults to "adjacency".

distance

A string specifying the chosen distance for calculating the test statistic, among: "hamming", "frobenius", "spectral" and "root-euclidean". Defaults to "frobenius".

stats

A character vector specifying the chosen test statistic(s), among: "original_edge_count", "generalized_edge_count", "weighted_edge_count", "student_euclidean", "welch_euclidean" or any statistics based on inter-point distances available in the flipr package: "flipr:student_ip", "flipr:fisher_ip", "flipr:bg_ip", "flipr:energy_ip", "flipr:cq_ip". Defaults to c("flipr:student_ip", "flipr:fisher_ip").

B

The number of permutation or the tolerance. If this number is lower than 1, it is intended as a tolerance. Otherwise, it is intended as the number of required permutations. Defaults to 1000L.

alpha

Significance level for hypothesis testing. Defaults to 0.05.

test

A character string specifying the formula to be used to compute the permutation p-value. Choices are "estimate", "upper_bound" and "exact". Defaults to "exact" which provides exact tests.

k

An integer specifying the density of the minimum spanning tree used for the edge count statistics. Defaults to 5L.

R

Number of Monte-Carlo trials used to estimate the power. Defaults to 1000L.

seed

An integer specifying the random generator seed. Defaults to '1234.

Details

Currently, six scenarios of pairs of populations are implemented. Scenario 0 allows to make sure that all our permutation tests are exact.

Value

A numeric value estimating the power of the test.

Examples

gnp_params <- list(p = 1/3)
k_regular_params <- list(k = 8L)
power2(
  model1_params = gnp_params,
  model2_params = k_regular_params,
  R = 10,
  B = 100,
  seed = 1234
)