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Repository
:package: exactt
Basic Info
- Host: GitHub
- Owner: ian-xu-economics
- License: other
- Language: R
- Default Branch: main
- Homepage: https://ian-xu-economics.github.io/exactt
- Size: 2.72 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created almost 2 years ago
· Last pushed 11 months ago
Metadata Files
Readme
License
Citation
README.Rmd
---
output: github_document
editor_options:
chunk_output_type: console
---
```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE,
comment = "#>",
fig.path = "man/figures/",
out.width = "100%")
```
exactt 
=========================================================
[](https://CRAN.R-project.org/package=exactt)
## Introduction
The `exactt` package tests whether a slope coefficient is equal to some null value using the novel method described in Pouliot (2023). Importantly, inverting such a test produces a marginally valid confidence interval.
## Installation
The `exactt` package is hosted on GitHub at https://github.com/ian-xu-economics/exactt/. It can be installed using the `remotes::install_github()` function:
``` r
# install.packages("remotes")
remotes::install_github("ian-xu-economics/exactt")
```
## Attribution
To cite the `exactt` package in publications, use the `citation()` function, which provides both the text version and the BibTeX entry for referencing:
``` r
citation("exactt")
```
## Using `exactt`
After installing `exactt`, we can attach the package to our session using the base `library()` function:
```{r}
library("exactt")
```
## Example Usage: Regular Case
To compute the $(1-\alpha)$ confidence interval, use the `exactt()` function. Here's an example looking at the effect of vitamin C on tooth growth in guinea pigs using data from `datasets::ToothGrowth`. We'll investigate the relationship between `supp` (orange juice (OJ) or ascorbic acid (VC)) and `dose` (dose in milligrams/day) on `len` (tooth length).
```{r}
summary(datasets::ToothGrowth)
```
Suppose our model is $len_i = \beta_0 + \beta_{dose} \times dose_i + \beta_{supp} \times supp_i + \varepsilon_i$. We can create a 90% confidence interval by plugging in standard formula notation into `exactt()`. The level of significance (alpha) equals 0.1 here, but if we choose not to specify any additional parameters, then by default:
* The number of blocks used equals 5 (`nBlocks = 5`).
* The confidence interval is constructed for all variables (`variables = NULL`).
* The number of permutations is equal to (`nPerms = factorial(nBlocks)`).
* The level of significance equals 0.05 (`alpha = 0.05`).
* The test statistics are studentized (`studentize = TRUE`).
* The ordering of the data is not permuted (`permutation = NULL`).
* The ordering of the data is not optimized (`optimize = FALSE`).
```{r}
exactt.1 <- exactt(model = len ~ dose + supp,
data = datasets::ToothGrowth,
alpha = 0.1)
print(exactt.1, digits = 5)
```
## Focusing on Specific Variables
To focus on specific coefficients, set the `variables` parameter. The number entered corresponds to the index of the regressors in the model (note that the intercept is never counted). For example, set `variables = 1` for `dose`, and set `variables = 2` for `supp`.
```{r}
exactt.2 <- exactt(model = len ~ dose + supp,
data = datasets::ToothGrowth,
alpha = 0.1,
variables = 1)
print(exactt.2, digits = 5)
```
This creates a 90% confidence interval for `dose` only. It is equivalent to the case where `variables = NULL` (all variables are of interest) because these confidence intervals are marginally valid.
## Model Flexibility
The `exactt()` function is designed to allow for easy modification of your model. For instance, you can treat a variable as categorical, include polynomial terms, or apply other transformations directly within the model formula. This flexibility helps tailor the analysis to specific research questions without needing pre-transformed data. To illustrate, consider treating `dose` as a categorical variable to explore its discrete impact on tooth length:
```{r}
exactt.3 <- exactt(model = len ~ as.factor(dose) + supp,
data = datasets::ToothGrowth,
alpha = 0.1)
exactt.3
```
The 90% confidence intervals when `dose` equals "2" and `supp` equals "VC" is not informative due to suboptimal data ordering, which can diminish the statistical power of the test. This issue can be addressed by optimizing the data ordering.
## Optimizing Data Ordering
The confidence intervals produced by the `exactt()` function can change with the ordering of the data. Certain data orderings can enhance statistical power, particularly when the sample size is small and the number of blocks is large. The impact of optimization is even more pronounced when dealing with categorical variables, where appropriate ordering can substantially increase the test's power.
The `exactt()` function utilizes a genetic algorithm (provided by the `GA::ga()` function) to optimize data ordering. This approach systematically explores various data arrangements to find the one that maximizes statistical power on average.
## Enabling Optimization
To activate the optimization feature, set `optimize = TRUE`. Additionally, `exactt()` allows for the specification of various parameters of the `GA::ga()` function to tailor the optimization process. For instance, you can limit the number of iterations with `maxiter` or specify the seed with `seed` for reproducibility:
```{r}
exactt.4 <- exactt(model = len ~ as.factor(dose) + supp,
data = datasets::ToothGrowth,
alpha = 0.1,
optimize = TRUE,
parallel = FALSE,
maxiter = 5,
seed = 2024)
print(exactt.4, digits = 5)
```
Note that by optimizing the data ordering, `exactt()` is now able to construct informative 90% confidence intervals for each category of `dose` and `supp` when they equal "2" and "VC" respectively. Furthermore, the detailed results of the optimization process, including the genetic algorithm's configurations and outcomes for each variable, are stored in the `exactt.4$gaResults`. For instance, to review a summary of the genetic algorithm's performance for the `suppVC` variable, use:
```{r}
exactt.4$gaResults$suppVC@summary
```
### Note on Optimization Effects
While optimization generally improves statistical power, it is essential to remember that it increases the average power and may not universally reduce the confidence interval's width in every instance.
## Example Usage: IV Case
The `exactt()` function is capable of handling models with instrumental variables (IV). In Example 15.5 of Wooldridge (2020), Wooldridge reanalyzes Mroz (1987). This example explores the impact of education (`educ`) on `log(wage)`, using parental education levels—mother's education (`motheduc`) and father's education (`fatheduc`)—as instruments. The model controls for experience (`exper`) and its square (`expersq`), with education being the primary variable of interest, hence we set variables = 1. Optionally, as before, we can optimize the data ordering to enhance statistical power.
```{r}
exactt.iv <- exactt(model = lwage ~ educ + exper + expersq | exper + expersq + motheduc + fatheduc,
data = wooldridge::mroz,
variables = 1,
optimize = TRUE,
parallel = FALSE,
maxiter = 10,
monitor = TRUE,
seed = 31740)
exactt.iv
```
Owner
- Name: Ian
- Login: ian-xu-economics
- Kind: user
- Repositories: 1
- Profile: https://github.com/ian-xu-economics
Citation (CITATION.cff)
# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
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title: 'exactt: Implement the Exact t-Test'
version: 4.1.0
abstract: A paragraph providing a full description of the project (on several lines...)
authors:
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given-names: Guillaume
- family-names: Xu
given-names: Ian
email: ianxu@uchicago.edu
preferred-citation:
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authors:
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given-names: "Guillaume"
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given-names: "Ian"
year: '2024'
notes: R package version 1.2.2
url: https://github.com/ian-xu-economics/exactt
repository-code: https://github.com/ian-xu-economics/exactt
url: https://github.com/ian-xu-economics/exactt
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DESCRIPTION
cran
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