gaussplotR

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R - Published in JOSS (2021)

https://github.com/vbaliga/gaussplotr

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Published in Journal of Open Source Software

Keywords

2d-gaussian gaussian gaussian-fit gaussian-interpolation gaussian-plot gaussian-volume plotting r rstats

Last synced: 4 months ago · JSON representation ·

Repository

🔔 Fit, predict, and plot 2D Gaussians in R

Basic Info

Host: GitHub
Owner: vbaliga
License: gpl-3.0
Language: HTML
Default Branch: master
Homepage: https://vbaliga.github.io/gaussplotR/
Size: 10.2 MB

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Stars: 4
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 13

Topics

2d-gaussian gaussian gaussian-fit gaussian-interpolation gaussian-plot gaussian-volume plotting r rstats

Created over 5 years ago · Last pushed over 4 years ago

Metadata Files

Readme Changelog Contributing License Citation Codemeta

README.Rmd

---
output: github_document
---



```{r opts, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  dpi = 300
)
```

# gaussplotR 


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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4041073.svg)](https://doi.org/10.5281/zenodo.4041073)
[![CRAN status](https://www.r-pkg.org/badges/version/gaussplotR)](https://CRAN.R-project.org/package=gaussplotR)


`gaussplotR` provides functions to fit two-dimensional Gaussian functions,
predict values from such functions, and produce plots of predicted data.

## Installation

You can install `gaussplotR` from CRAN via:

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

Or to get the latest (developmental) version through GitHub, use:
  
``` {r install_github, eval = FALSE}
devtools::install_github("vbaliga/gaussplotR")
```


## Example

The function `fit_gaussian_2D()` is the workhorse of `gaussplotR`. It uses
`stats::nls()` to find the best-fitting parameters of a 2D-Gaussian fit to
supplied data based on one of three formula choices. The function
`autofit_gaussian_2D()` can be used to automatically figure out the best formula
choice and arrive at the best-fitting parameters.

The `predict_gaussian_2D()` function can then be used to predict values from
the Gaussian over a supplied grid of X- and Y-values (generated here via 
`expand.grid()`). This is useful if the original data is relatively sparse and
interpolation of values is desired.

Plotting can then be achieved via `ggplot_gaussian_2D()`, but note that the 
`data.frame` created by `predict_gaussian_2D()` can be supplied to other 
plotting frameworks such as `lattice::levelplot()`. A 3D plot can also be 
produced via `rgl_gaussian_2D()` (not shown here).

```{r example}
library(gaussplotR)

## Load the sample data set
data(gaussplot_sample_data)

## The raw data we'd like to use are in columns 1:3
samp_dat <-
  gaussplot_sample_data[,1:3]


#### Example 1: Unconstrained elliptical ####
## This fits an unconstrained elliptical by default
gauss_fit_ue <-
  fit_gaussian_2D(samp_dat)

## Generate a grid of X- and Y- values on which to predict
grid <-
  expand.grid(X_values = seq(from = -5, to = 0, by = 0.1),
              Y_values = seq(from = -1, to = 4, by = 0.1))

## Predict the values using predict_gaussian_2D
gauss_data_ue <-
  predict_gaussian_2D(
    fit_object = gauss_fit_ue,
    X_values = grid$X_values,
    Y_values = grid$Y_values,
  )

## Plot via ggplot2 and metR
library(ggplot2); library(metR)
ggplot_gaussian_2D(gauss_data_ue)

## And another example plot via lattice::levelplot()
library(lattice)
lattice::levelplot(
  predicted_values ~ X_values * Y_values,
  data = gauss_data_ue,
  col.regions = colorRampPalette(
    c("white", "blue")
    )(100),
  asp = 1
)

#### Example 2: Constrained elliptical_log ####
## This fits a constrained elliptical, as in Priebe et al. 2003
gauss_fit_cel <-
  fit_gaussian_2D(
    samp_dat,
    method = "elliptical_log",
    constrain_orientation = -1
  )

## Generate a grid of x- and y- values on which to predict
grid <-
  expand.grid(X_values = seq(from = -5, to = 0, by = 0.1),
              Y_values = seq(from = -1, to = 4, by = 0.1))

## Predict the values using predict_gaussian_2D
gauss_data_cel <-
  predict_gaussian_2D(
    fit_object = gauss_fit_cel,
    X_values = grid$X_values,
    Y_values = grid$Y_values,
  )

## Plot via ggplot2 and metR
ggplot_gaussian_2D(gauss_data_cel)

```

Should you be interested in having `gaussplotR` try to automatically determine
the best choice of `method` for `fit_gaussian_2D()`, the `autofit_gaussian_2D()`
function can come in handy. The default is to select the `method` that 
produces a fit with the lowest `rmse`, but other choices include `rss` and 
`AIC`.

```{r autofit}
## Use autofit_gaussian_2D() to automatically decide the best 
## model to use
gauss_auto <-
  autofit_gaussian_2D(
    samp_dat,
    comparison_method = "rmse", 
    simplify = TRUE
    )

## The output has the same components as `fit_gaussian_2D()` 
## but for the automatically-selected best-fitting method only:
summary(gauss_auto)

```

## Contributing and/or raising Issues

Feedback on bugs, improvements, and/or feature requests are all welcome. 
Please see the Issues templates on GitHub to make a bug fix request or feature 
request.

To contribute code via a pull request, please consult the Contributing Guide 
first.


## Citation

Baliga, VB. 2021. gaussplotR: Fit, predict, and plot 2D-Gaussians in R. Journal of Open Source Software, 6(60), 3074. https://doi.org/10.21105/joss.03074


## License

GPL (>= 3) + file LICENSE

🐢

Owner

Name: Vikram Baliga
Login: vbaliga
Kind: user
Location: Vancouver, B.C.
Company: @ubc

Website: https://vbaliga.github.io/
Repositories: 3
Profile: https://github.com/vbaliga

Animal locomotory behavior + (macro)evolution; Research Associate at @ubc Zoology

JOSS Publication

gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

Published

April 06, 2021

DOI

10.21105/joss.03074

Volume 6, Issue 60, Page 3074

Authors

Vikram B. Baliga

Department of Zoology, University of British Columbia, Vancouver, British Colombia, Canada V6T 1Z4

Editor

Kristina Riemer

Citation (CITATION.cff)

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  orcid: "https://orcid.org/0000-0002-9367-8974"
title: "gaussplotR"
version: 0.2.6
doi: 10.21105/joss.03074
date-released: 2021-07-29
url: "https://github.com/vbaliga/gaussplotR"

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cran.r-project.org: gaussplotR

Fit, Predict and Plot 2D Gaussians

Homepage: https://github.com/vbaliga/gaussplotR
Documentation: http://cran.r-project.org/web/packages/gaussplotR/gaussplotR.pdf
License: GPL-3
Latest release: 0.2.5
published over 4 years ago

Versions: 5
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Dependent Repositories: 0
Downloads: 262 Last month

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Last synced: 4 months ago

Dependencies

DESCRIPTION cran

R >= 3.3.0 depends
ggplot2 >= 3.3.0 imports
metR >= 0.7.0 imports
rgl * imports
viridisLite * imports
knitr * suggests
lattice * suggests
rmarkdown * suggests
testthat * suggests

gaussplotR

Science Score: 100.0%

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gaussplotR: Fit, Predict and Plot 2D-Gaussians in R

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