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ggfan

Jason Hilton 05 March, 2019

Summarise a distribution through coloured intervals

This package is a ggplot-based implementation of some of the functionality of the fanplot package by Guy Abel. Fanplot provides methods to visualise probability distributions by representing intervals of the distribution function with colours. Plotting samples from posterior distributions obtained through MCMC is a particular aim. A ggplot-based implementation is useful as it allows leverage of the power of ggplot2 features allowing, for example, facetting and easy theming.

Installation

Installation directly from CRAN should be possible shortly, once the package has been resubmitted and checked: install.packages("ggfan") Otherwise, the package can be installed directly from github using the devtools package: devtools::install_github("jasonhilton/ggfan").

Quick Start

The provided fake_df data gives and example of the type of data you might want to plot with ggfan: it consists of 1000 samples of an outcome variable of interest at each value of a covariate x. We can plot this simply using standard ggplot2 syntax and geom_fan. Convenient ggplot features such as themes, colour scales and facetting can also be used.

ggplot(fake_df, aes(x=x, y=y)) + geom_fan() + theme_minimal() + scale_fill_distiller(palette="Spectral")

Further Information

A full description of the functionality of ggfan is provided in the vignette, provided here.

A brief example of using ggfan to plot stan output is given in a second vignette here

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Install

install.packages('ggfan')

Monthly Downloads

42

Version

0.1.3

License

GPL-2 | file LICENSE

Issues

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Maintainer

Jason Hilton

Last Published

March 7th, 2019

Functions in ggfan (0.1.3)

geom_fan

Fan plot visualising intervals of a distribution
geom_interval

Line plot visualising intervals of a distribution
calc_quantiles

Calculate quantiles of a tidy dataframe
StatSample

GeomIntervalPath

ggfan

Fanplots for ggplot2
GeomIntervalPoly

gp_model_fit

A stan_fit object used in the ggfan_stan vignette, containing posterior samples from a latent gaussian process model. This is provided as data to avoid having to conduct computationally expensive sampling when producing the vignettes.
.in_numeric

Find elements of one numeric vector in another.
fake_df

Fake dataset intended to resemble a set of MCMC samples of a variable over one covariate (perhaps time),
stat_interval

Line plot visualising intervals of a distribution
stat_sample

StatInterval

StatIntervalFctr