knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

As mentioned in the other vignettes, the *openEBGM* package is capable of
calculating $EBGM$ and quantile scores from the posterior distribution.
*openEBGM* makes it easy to calculate such quantities using a class and object
system. While creation of objects of class openEBGM is not necessary (see
previous vignette), it provides access to methods for some common generic
functions and reduces the number of function calls needed.

To create the object, we first need to calculate the hyperparameter estimates.

library(openEBGM) data(caers) proc <- processRaw(caers, stratify = FALSE, zeroes = FALSE) squashed <- squashData(proc) theta_init <- data.frame(alpha1 = c(0.2, 0.1, 0.3, 0.5, 0.2), beta1 = c(0.1, 0.1, 0.5, 0.3, 0.2), alpha2 = c(2, 10, 6, 12, 5), beta2 = c(4, 10, 6, 12, 5), p = c(1/3, 0.2, 0.5, 0.8, 0.4) ) hyper_estimate <- autoHyper(squashed, theta_init = theta_init, zeroes = FALSE, squashed = TRUE, N_star = 1)

Once we have the hyperparameter estimates and the processed data, we can calculate the $EBGM$ scores and any desired quantile(s) from the posterior distribution.

ebout <- ebScores(proc, hyper_estimate = hyper_estimate, quantiles = c(5, 95)) #For the 5th and 95th percentiles ebout_noquant <- ebScores(proc, hyper_estimate = hyper_estimate, quantiles = NULL) #For no quantiles

As seen above, we can calculate the $EBGM$ scores with or without adding quantiles. If using quantiles, we can specifying any number of quantiles.

Once the object has been created, we can use class-specific methods for some of
R's generic functions (namely, `print()`

, `summary()`

, and `plot()`

).

#We can print an openEBGM object to get a quick look at the contents print(ebout) print(ebout_noquant, threshold = 3)

When quantiles are present, simply printing the object shows, by default, how
many *var1-var2* pairs exist that have QUANT$>x$, where $x$ is the minimum
quantile threshold used for the data (default 2). In the absence of quantiles,
it simply outputs the number of *var1-var2* pairs that have an $EBGM$ score
greater than the specified threshold. In both cases, it also shows a quick look
at the *var1-var2* pairs with the highest $x$ or $EBGM$, depending on whether
quantiles were calculated or not.

One can also use the `summary()`

function on an openEBM object to get further
information about the calculations.

```
summary(ebout)
```

As seen above, by default the `summary()`

function, when called on an openEBGM
object, outputs some descriptive statistics on the $EBGM$ and quantile scores,
and a histogram of the $EBGM$ scores. There are options to disable plot output,
or to calculate the log~2~ transform of the scores, which provides a Bayesian
information statistic (when applied to the $EBGM$ score).

summary(ebout, plot.out = FALSE, log.trans = TRUE)

Finally, *openEBGM* provides a method for the `plot()`

function that can produce
a variety of different plots. These are shown below.

```
plot(ebout)
```

As seen, by default, the `plot()`

function shows the top $EBGM$ scores by
*var1-var2* combinations (only *var1* is shown for space preservation) and
"error bars" using the lowest and highest quantiles calculated. The sample
size for each *var1-var2* combination is also plotted.

A specific event from *var2* may also be selected, and only the *var1-var2*
combinations that include this particular event will be shown. An example is
shown below.

plot(ebout, event = "CHOKING")

In addition to the bar chart, the `plot()`

function can also create a histogram
of the $EBGM$ scores.

plot(ebout, plot.type = "histogram")

Again, one may choose an event from *var2* by which to subset the data when
plotting.

plot(ebout, plot.type = "histogram", event = "CHOKING")

Finally, the last type of plot included with the `plot()`

function shows the
shrinkage performed by the algorithm. It is called the "Chirtel Squid Plot",
titled after its creator, Stuart Chirtel.

plot(ebout, plot.type = "shrinkage")

While a specific event may be selected by which to subset the data, it can lead to a less informative plot due to smaller sample size.

plot(ebout, plot.type = "shrinkage", event = "CHOKING")

*openEBGM* was designed to give the user a high level of control over data
analysis choices (stratification, data squashing, etc.) using DuMouchel's
Gamma-Poisson Shrinkage (GPS) method
(*1999, https://doi.org/10.1080/00031305.1999.10474456*),
(

`https://doi.org/10.1145/502512.502526`

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