hypothesize: Statistical Tests in R

hypothesize is a simple hypothesis testing API in R. It is mostly designed to be used by other libraries so that they can wrap their own hypothesis tests in a consistent way.

We define the API as a set of generic methods. We also provide implementations for the likelihood ration test (LRT) and the Wald test.

Installation

You can install the development version of hypothesize from GitHub with:

# install.packages("devtools")
devtools::install_github("queelius/hypothesize")

Load the Package

library(hypothesize)

The hypothesize API

hypothesize defines an API for retrieving hypothesis test results. An object satisfies the concept of a hypothesis test if it implements the following generic methods:

• pval(): Extracts the p-value from an object that models a hypothesis test.

• dof(): Retrieves the degrees of freedom associated with a hypothesis test.

• test_stat(): Obtains the test statistic from the hypothesis test.

• is_significant_at(): Determines if the hypothesis test is significant at a specified significance level.

Implementation: hypothesis_test

We provide an implementations for hypothesize. It it has a constructor that takes a statistical test (stat), p-value (p.value), a degree-of-freedom (dof), and optionally a list of superclasses and any additional arguments that will be passed into the object. Here is its type signature:

`hypothesis_test <- function(stat, p.value, dof, superclasses = NULL, ...) `

It creates a hypothesis_test object that implements all of the generic methods required by hypothesize. The hypothesis_test object also implements print for summary outputs.

We use this constructor for two tests we implement, the LRT and Wald tests:

• lrt(): Performs a Likelihood Ratio Test based on log-likelihood values from nested models.

• wald_test(): Performs a Wald test to compare a parameter estimate to a specified value.

Example: Using lrt

The lrt function is particularly useful for comparing nested models — where one model (the null model) is a special case of another (the alternative model).

Scenario

Suppose we have two models that aim to explain the same dataset. Model 1 (the null model) is simpler, with fewer parameters, while Model 2 (the alternative model) includes additional parameters. We wish to test if the complexity of Model 2 is justified by a significantly better fit to the data.

Step-by-Step Example

1. Define Log-Likelihoods: Assume we have calculated the log-likelihoods for both models on the same dataset. For the null model, the log-likelihood is -100, and for the alternative model, it is -99. Assume that the difference in degrees of freedom between the two models is 2.

2. Perform LRT: We use lrt to perform the Likelihood Ratio Test.

# Perform LRT
stat <- lrt(null_loglik = -100, alt_loglik = -96.105, dof = 3)
print(stat)
#> Hypothesis test ( likelihood_ratio_test )
#> -----------------------------
#> Test statistic:  7.79 
#> P-value:  0.0506 
#> Degrees of freedom:  3 
#> Significant at 5% level:  FALSE

We show the output of the stat object, which includes all the relevant information about the test. However, we might want to look at its parts independently, particularly if we need programmatic accees to relevant parts of the test.

1. Evaluate Significance: Determine if the difference in log-likelihoods is significant at the 5% level.

# Check significance
is_significant_at(stat, 0.05)
#> [1] FALSE

A negative test result indicates that the alternative model is not compatible with the data at the 5% significance level. However, we might want to extract the test statistic, p-value, and degrees of freedom to arrive at a more nuanced interpretation.

1. Examine the Test Result: Extract and examine the test statistic, p-value, and degrees of freedom to evaluate the significance.

# Extract test statistic
test_stat(stat)
#> [1] 7.79

# Extract p-value
pval(stat)
#> [1] 0.0506

# Extract degrees of freedom
dof(stat)
#> [1] 3

We see that the p-value is only slightly above our (arbitrarily) specified α = 0.05. This suggests that the alternative model may be reasonable to consider, but it is not a clear-cut decision. In practice, we would likely want to consider other factors, such as the practical significance of the additional complexity, or collecting more data to reduce uncertainty, before making a final decision.

Example: Using Wald Test

The Wald test is also implemented in hypothesize. Tis test is used to compare the value of a parameter to a specified value, and is often used in the context of regression models.

# Example: Wald Test
print(wald_test(estimate = 1.5, se = 0.5, null_value = 1))
#> Hypothesis test ( wald_test )
#> -----------------------------
#> Test statistic:  1 
#> P-value:  0.317 
#> Degrees of freedom:  1 
#> Significant at 5% level:  FALSE