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Power Simulations for Permutation Tests

Source: R/simulations.R
power2.Rd

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

Usage

power2(
  sample_size1,
  model1,
  params1,
  sample_size2,
  model2,
  params2,
  representation = "adjacency",
  distance = "frobenius",
  stats = c("flipr:t_ip", "flipr:f_ip"),
  B = 1000L,
  alpha = 0.05,
  test = "exact",
  k = 5L,
  R = 1000L
)

Arguments

sample_size1

An integer specifying the size of the first sample.

model1

A string specifying the model to be used for sampling networks in the first sample. All tidygraph::play_ functions are supported. The model name corresponds to the name of the function without the play_ prefix.

params1

A list specifying the parameters to be passed to the model function that will generate the first sample.

sample_size2

An integer specifying the size of the second sample.

model2

A string specifying the model to be used for sampling networks in the second sample. All tidygraph::play_ functions are supported. The model name corresponds to the name of the function without the play_ prefix.

params2

A list specifying the parameters to be passed to the model

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.

Value

A numeric value estimating the power of the test.

Details

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

Examples

gnp_params <- list(n = 24L, p = 1/3)
degree_params <- list(out_degree = rep(2, 24L), method = "configuration")
power2(
  sample_size1 = 10L, model1 = "gnp", params1 = gnp_params,
  sample_size2 = 10L, model2 = "degree", params2 = degree_params,
  R = 10L,
  B = 100L
)
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> ℹ Calling the `tidygraph::play_gnp()` function with the following arguments:
#> • n: 24
#> • p: 0.333333333333333
#> • directed: TRUE
#> • loops: FALSE
#> ℹ Calling the `tidygraph::play_degree()` function with the following arguments:
#> • out_degree: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …, 2, and 2
#> • method: configuration
#> • in_degree: NULL
#> ! Setting the seed for sampling permutations is mandatory for obtaining a continuous p-value function. Using `seed = 1234`.
#> [1] 1