PredTest (version 0.1.0)

pred_test: Predictive Test Function

Description

This function performs statistical tests to determine the predictive power of a results set weighted by a corresponding vector of weights. It offers various methods to conduct the test, allowing flexibility depending on the data characteristics and analysis requirements.

Usage

pred_test(
  weights_vector,
  results_vector,
  test_type = "exact",
  phi_0 = 0.5,
  sims = 5000
)

Value

A list containing:

num_correctly_predicted

The number of results correctly predicted as per the specified criteria.

p_value

The p-value resulting from the test, indicating the probability of observing the test results under the null hypothesis.

test_stat

The test statistic calculated based on the weights and results.

p0

The estimated proportion derived from the weights and results.

ci

A confidence interval for the estimated proportion derived from the weights and results using the Wilson score method.

Arguments

weights_vector

A numeric vector where each element represents the weight for a corresponding result in the results vector. Each value must be on the interval \([1/m, 1]\), where \(m = 1/length(weights_vector)\).

results_vector

A numeric vector of test results where each element is in the set {0, 1}, representing the binary outcome of each prediction.

test_type

A character string specifying the type of statistical test to perform. The valid options are 'exact', 'approx', or 'bootstrap'.

phi_0

A numeric value on the interval (0, 1) representing the null hypothesis value against which the test results are compared.

sims

A natural number that specifies the number of simulations to perform when the bootstrap method is chosen. This parameter allows control over the robustness of the bootstrap approximation.

Details

This function performs error handling to ensure appropriate input values and types. It then calculates the test statistic and evaluates the p-value based on the specified test type.

Examples

# Example weights and results vectors
weights_vector <- c(1/3, 0.5, 1)
results_vector <- c(0, 1, 1)

# Exact test
result_exact <- pred_test(weights_vector, results_vector, test_type = 'exact')
result_exact

# Approximate test
result_approx <- pred_test(weights_vector, results_vector, test_type = 'approx')
result_approx

# Bootstrap test
result_bootstrap <- pred_test(weights_vector, results_vector, test_type = 'bootstrap')
result_bootstrap