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title: "An Introduction to the nullabor package"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{An Introduction to the nullabor package}
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---  


An introduction to the **nullabor** package
=======================================


```{r setup, include=FALSE}
library(knitr)
opts_chunk$set(out.extra='style="display:block; margin: auto"', fig.align="center")
```

The  **nullabor** package provides functions to support quantifying the significance of structure seen in plots of data. 

```{r message=FALSE}
library(nullabor)
library(ggplot2)
library(dplyr)
```


The functions provide methods for two protocols: ***lineup*** and ***rorschach***. The lineup protocol places the plot of the actual data among a field of plots of null data, and in the Rorschach, all plots are of null data. The ***encrypt*** function enables the location of the actual data plot to be a secret, that needs ***decrypt*** to reveal. There are several different functions for generating null data sets: ***null_permute***, ***null_lm*** and ***null_dist***. 


The lineup protocol
-------------------

In this protocol, the plot of the real data is randomly embedded amongst a set of null plots. The matrix of plots is known as a lineup. The null plots are generated by a method consistent with the null hypothesis. The lineup is shown to an observer. If the observer can pick the real data as different from the others, this puts weight on the statistical significance of the structure in the plot. The `"lineup"` function returns a set of generated null datasets and the real data embedded randomly among these null datasets. The method of null generation should be provided in the lineup function for the null datasets to be generated automatically along with the real dataset. The users also have the option of generating the null datasets themselves and providing them in the `"lineup"` function. The position of the real dataset can be left missing and the function picks the position at random. The function then returns the position as an encrypted code. The encrypted code is copied and pasted on the console to obtain the true position of the plot.


```{r}
d <- lineup(null_permute("mpg"), mtcars)
head(d)
# Position of actual data plot
attr(d, "pos")
```

The lineup data can be then used to generate the lineup using **ggplot2**. The lineup is shown to one or more observers who are asked to identify the plot which is different. If the observer can identify the plot of the real data correctly, we reject the null hypothesis and conclude that the plot of the real data has stronger structure than the null plots.

```{r, fig.height = 10, fig.width = 11}
ggplot(data=d, aes(x=mpg, y=wt)) + geom_point() + facet_wrap(~ .sample)
```

The Rorschach protocol
----------------------

The Rorschach protocol is used to calibrate the eyes for variation due to sampling. The plots generated corresponds to the null datasets, data that is consistent with a null hypothesis. The `"rorschach"` function returns a set of null plots which are shown to observers to calibrate their eyes with variation. Like the `"lineup"` function, the null generating mechanism should be provided as an input along with a real dataset. A probability can also be given as input which dictates the chance of including the true data with null data.

```{r, fig.height = 10, fig.width = 11}
d <- rorschach(null_permute("mpg"), mtcars, n = 20, p = 0)
ggplot(data=d, aes(x=mpg, y=wt)) + geom_point() + facet_wrap(~ .sample)
```

Generate null data with a specific distribution
------------------------------------------------

The `"null_dist"` function takes as input a variable name of the data and a particular distribution. This variable in the data is substituted by random generations of the particular distribution. The different distributions include beta, cauchy, chi-squared, exponential, f, gamma, geometric, log-normal, lognormal, logistic, negative binomial, normal, poisson, t and weibull. A list of parameters of distribution can also be provided as input. In case it is not provided, `"fitdistr"` is used to estimate the parameters from the given data. The function `"null_dist"` returns a function that given the data generates a null data set.

```{r}
head(null_dist("mpg", dist = "normal")(mtcars))
```

Generate null data by permuting a variable
-------------------------------------------

The `"null_permute"` function takes as input a variable name of the data. This variable is permuted to obtain the null dataset. The function `"null_dist"` returns a function that given the data generates a null data set.

```{r}
head(null_permute("mpg")(mtcars))
```

Generate null data with null residuals from a model
---------------------------------------------------

The function `"null_lm"` takes as input a model specification formula as defined by `"lm"` and method for generating null residuals from the model. The three built in methods are 'rotate', 'pboot' and 'boot' defined by `"resid_rotate"`, `"resid_pboot"` and `"resid_boot"` respectively. The function returns a function which given the data generates a null dataset.

```{r}
head(null_lm(wt~mpg, method = 'rotate')(mtcars))
```

Generate null data outside of nullabor
--------------------------------------

If the null generating mechanism is specialised it is worth creating the process outside of the nullabor package. The `lineup` function can be simply used to randomize the position of the data plot to produce a lineup, encrypting the position. An example of this is in Roy Chowdhury, N. et al (2015), where the data is high-dimensional, and the response variable is categorical. We would use a dimension reduction method like linear discrimants to find a low-dimensional projection that separates the groups. To generate null data, the group labels are permuted and the dimension reduction is performed again. 

```{r}
library(MASS)

data(wasps)

wasp.lda <- lda(Group~., data=wasps[,-1])
wasp.ld <- predict(wasp.lda, dimen=2)$x
true <- data.frame(wasp.ld, Group=wasps$Group)

wasp.sim <- data.frame(LD1=NULL, LD2=NULL, Group=NULL, .n=NULL)
for (i in 1:19) {
  x <- wasps
  x$Group <- sample(x$Group)
  x.lda <- lda(Group~., data=x[,-1])
  x.ld <- predict(x.lda, dimen=2)$x
  sim <- data.frame(x.ld, Group=x$Group, .n=i)
  wasp.sim <- rbind(wasp.sim, sim)
}

pos <- sample(1:20, 1)
d <- lineup(true=true, samples=wasp.sim, pos=pos)
ggplot(d, aes(x=LD1, y=LD2, colour=Group)) + 
  facet_wrap(~.sample, ncol=5) +
  geom_point() + theme(aspect.ratio=1)

attr(d, "pos")
```

Computing p-values
------------------

The function `pvisual` can be used to compute a visual p-value after the lineup has been shown to a group of independent observers. You need to provide (1) number of plots in the lineup, m (default=20), (2) number of observers who read the plot, K, and (3) the number of observers who picked the data plot. For the above example, suppose that 10 observers evaluated the lineup, and of these 4 selected the data plot, then the visual p-value, calculated using the binomial, and via simulation to account for dependencies is:

```{r}
pvisual(4, 10)
```

Computing power
---------------

The function `visual_power` can be used to compute the power of each lineup. This is an example using the sample turk results provided.

```{r}
data(turk_results)
visual_power(turk_results)
```

References
----------

Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E.-K., Swayne, D. F, Wickham, H. (2009) Statistical Inference for Exploratory Data Analysis and Model Diagnostics, Royal Society Philosophical Transactions A, 367:4361--4383, DOI: 10.1098/rsta.2009.0120.
  
Wickham, H., Cook, D., Hofmann, H. and Buja, A. (2010) Graphical Inference for Infovis, IEEE Transactions on Visualization and Computer Graphics, 16(6):973--979, Best paper award, DOI: 10.1109/TVCG.2010.161. 

Hofmann, H., Follett, L., Majumder, M. and Cook, D. (2012) Graphical Tests for Power Comparison of Competing Designs, IEEE Transactions on Visualization and Computer Graphics, 18(12):2441--2448, DOI: 10.1109/TVCG.2012.230.

Majumder, M., Hofmann, H. and Cook, D. (2013) Validation of Visual Statistical Inference, Applied to Linear Models, Journal of the American Statistical Association, 108(503):942--956, Featured Article, DOI: 10.1080/01621459.2013.808157.

Roy Chowdhury, N., Cook, D., Hofmann, H., Majumder, M., Lee, E. K., & Toth, A. L. (2014). Using visual statistical inference to better understand random class separations in high dimension, low sample size data. Computational Statistics, 30(2), 293-316, DOI: 10.1007/s00180-014-0534-x.

Majumder, M. and Hofmann, H. and Cook, D. (2014) Human Factors Influencing Visual Statistical Inference,
DOI: 10.48550/arXiv.1408.1974.
