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nullabor: lineup and Rorschach plots for visual inference

Di Cook, Hadley Wickham, Niladri Roy Chowdhury, Heike Hofmann February 26, 2020

nullabor

Tools for graphical inference: prevent fooling yourself with the Rorschach protocol and check the surprising features in your data with the lineup protocol!

Installation

The nullabor package can be installed from CRAN

install.packages("nullabor")

You can install the development version of nullabor from github with:

# install.packages("devtools")
devtools::install_github("dicook/nullabor")

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Install

install.packages('nullabor')

Monthly Downloads

1,427

Version

0.3.12

License

GPL (>= 2)

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Repository

https://github.com/dicook/nullabor

Maintainer

Last Published

July 21st, 2024

Functions in nullabor (0.3.12)

Sample size calculator

A theme to minimally strip away the context

Distance for univariate data

Sample turk results

Distance based on separation of clusters

Rotation residuals.

Los Angeles Lakers play-by-play data.

Parametric bootstrap residuals.

Find plot data. If data is not specified, this function will attempt to find the data corresponding to the last ggplot2 created or displayed. This will work in most situations where you are creating the plot and immediately displaying it, but may not work in other situations. In those cases, please specify the data explicitly.

P-value calculations.

Wasp gene expression data.

Power calculations.

The Rorschach protocol.

Residuals simulated by a normal model, with specified sigma

Add true data into data frame containing null data sets.

Calculating the mean distances of each plot in the lineup.

Finds the number of bins in x and y direction which gives the maximum binned distance.

Generate null data with a specific distribution.

Calculating the difference between true plot and the null plot with the maximum distance.

The line-up protocol.

Plotting the distribution of the distance measure

Binned Distance

Distance based on side by side Boxplots

Conversion rate of 1 Australian Doller (AUD) to 1 US Dollar

Polls and election results from the 2012 US Election

Generate null data by permuting a variable.

Generate null data with null residuals from a model.

Computing th distance for the null plots

Generate null data by simulating from a time series model.

Use decrypt to reveal the position of the real data.

Empirical distribution of the distance

Distance based on the regression parameters

Bootstrap residuals.