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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "75%"
)
```
# ppdiag
<!-- badges: start -->
[](https://github.com/OwenWard/ppdiag/actions)
[](https://CRAN.R-project.org/package=ppdiag)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://codecov.io/gh/OwenWard/ppdiag?branch=main)
[](https://doi.org/10.21105/joss.03133)
<!-- badges: end -->
``ppdiag`` is an `R` package which provides a collection of tools which
can be used to assess the fit of temporal point processes to data.
These currently include:
- Simulating data from a specified point process
- Fitting a specified point process model to data
- Evaluating the fit of a point process model to data using
several diagnostic tools
# Installation
You can install the released version of ppdiag from [CRAN](https://CRAN.R-project.org) with:
```{r, eval=FALSE}
install.packages("ppdiag")
```
The current development version of this
package is available from [GitHub](https://github.com/OwenWard/ppdiag) with:
```{r, eval=FALSE}
# install.packages("remotes")
remotes::install_github("OwenWard/ppdiag")
```
# Example
To illustrate some of the basic functionality of this package,
we can simulate data from a specified Hawkes process and examine
our diagnostic results when we fit a homogeneous Poisson process
to this data.
```{r example}
library(ppdiag)
hp_obj <- pp_hp(lambda0 = 0.2, alpha = 0.35, beta = 0.8)
sim_hp <- pp_simulate(hp_obj, end = 200)
sim_hp
```
We can readily evaluate the fit of a homogeneous Poisson process to this
data.
```{r fit_hpp}
est_hpp <- fithpp(sim_hp)
est_hpp
pp_diag(est_hpp, events = sim_hp)
```
```{r fit_hp}
hp_est <- fithp(events = sim_hp)
pp_diag(hp_est, events = sim_hp)
```
## Markov Modulated Hawkes Process Example
This is particularly useful for more complex point processes, such as the
Markov Modulated Hawkes Process (MMHP).
We can simulate events from this model
and examine the fit of simpler point processes to this data.
```{r mmhp example}
Q <- matrix(c(-0.2, 0.2, 0.1, -0.1), ncol = 2, byrow = TRUE)
mmhp_obj <- pp_mmhp(Q, delta = c(1 / 3, 2 / 3),
lambda0 = 0.2,
lambda1 = .75,
alpha = 0.4,
beta = 0.8)
mmhp_obj
mmhp_events <- pp_simulate(mmhp_obj, n = 50)
```
We can easily fit a homogeneous Poisson process and visualise the goodness of
fit.
```{r fit_hpp_to_mmhp}
est_hpp <- fithpp(events = mmhp_events$events)
pp_diag(est_hpp,mmhp_events$events)
```
Similarly for a Hawkes process.
```{r fit_hp_to_mmhp}
est_hp <- fithp(events = mmhp_events$events)
pp_diag(est_hp,mmhp_events$events)
```
We can then compare to the true point process model.
```{r fit_mmhp}
pp_diag(mmhp_obj, mmhp_events$events)
```
# Getting help and contributing
Please file any issues [here](https://github.com/OwenWard/ppdiag/issues).
Similarly, we would be delighted if anyone would like to contribute to
this package (such as adding other point processes, kernel functions).
Feel free to take a look
[here](https://github.com/OwenWard/ppdiag/blob/main/CONTRIBUTING.md)
and reach out with any questions.
# References
- Sun et al., (2021). ppdiag: Diagnostic Tools for Temporal Point Processes. Journal of Open Source Software, 6(61), 3133, https://doi.org/10.21105/joss.03133
- Wu et al. (2021), Diagnostics and Visualization of Point Process Models for Event Times on a Social Network,
In Applied Modeling Techniques and Data Analysis 1 (eds Y. Dimotikalis, A. Karagrigoriou, C. Parpoula and C.H. Skiadas).
https://doi.org/10.1002/9781119821588.ch7