gofreg

Bootstrap-based Goodness-of-Fit Tests for Parametric Regression

https://github.com/gkremling/gofreg

Repository

Bootstrap-based Goodness-of-Fit Tests for Parametric Regression

README.Rmd

---
output: github_document
---




[![R-CMD-check](https://github.com/gkremling/gofreg/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/gkremling/gofreg/actions/workflows/R-CMD-check.yaml)


```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# gofreg

This R package provides classes and methods to fit parametric regression models
to given data and to perform bootstrap-based goodness-of-fit tests using
different test statistics.

The data can either consist of $(X,Y)$ pairs of covariates and response 
variables or in case of random censorship it consists of tuples $(X, Z, \delta)$ 
denoting covariates, censored survival times and censoring indicators. The 
package includes different parametric regression models (mostly generalized 
linear models) and test statistics (based on different papers). It can easily be 
extended by other user-defined models and test statistics. 

## Installation

You can install it from CRAN

```{r, eval = FALSE}
install.packages("gofreg")
```

or github

``` {r, eval = FALSE}
devtools::install_github("gkremling/gofreg")
```

## Example

This is a basic example which shows how to fit a parametric regression model to 
a given dataset and afterwards perform a goodness-of-fit test. In this example, 
we use the dataset `datasets::cars`, a generalized linear model with normal 
distribution and the conditional Kolmogorov test statistic of the marginal 
distribution of $Y$ defined in Kremling & Dikta (2024) [arXiv:2409.20262](https://arxiv.org/abs/2409.20262).

```{r example}
library(gofreg)

set.seed(123)

data <- dplyr::tibble(x = datasets::cars$speed, y = datasets::cars$dist)
model <- GLM.new(distr = "normal", linkinv = identity)
model$fit(data, params_init = list(beta = 3, sd = 2), inplace = TRUE)
print(model$get_params())
gt <- GOFTest$new(data = data, model_fitted = model, test_stat = CondKolmY$new(), nboot = 100)
print(gt$get_pvalue())
gt$plot_procs()
```

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