---
title: "Distributions for Binomial-like and Poisson-like Counts"
author: "John Maindonald"
date: "`r format(Sys.Date(),'%d %B %Y')`"
output:
  bookdown::html_document2:
    theme: cayman
    highlight: vignette
    base_format: prettydoc::html_pretty
    toc: true
    toc_depth: 2
    number-sections: true
    pandoc_args: NULL
    link-citations: true
bibliography: qra.bib
vignette: >
  %\VignetteIndexEntry{Distributions for Binomial-like and Poisson-like Counts}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE, cache=FALSE}
library(knitr)
pdf.options(pointsize=12)
oldopt <- options(digits=4, formatR.arrow=FALSE, scipen=999, width=70)
opts_chunk$set(comment=NA, rmLeadingLF=TRUE, tidy.opts = list(replace.assign=FALSE), fig.align='center', crop=TRUE)
knitr::knit_hooks$set(crop = knitr::hook_pdfcrop)
read_chunk("discrete-code.R")
```

```{r set-lattice, echo=FALSE}
```
```{r echo=FALSE}
if("lme4" %in% (.packages()))
  detach("package:lme4", unload=TRUE)
req_suggested_packages <- c("gamlss")
PKGgamlss <- suppressMessages(require('gamlss', quietly = TRUE))
nogamlss <- !PKGgamlss
if (nogamlss) {
   message("This vignette requires the `gamlss` package, that is not available/installed.")
message("Code that requires this package will not be executed.")
}
```

```{r, include = FALSE}
op <- options(width=90)
knitr::opts_chunk$set(
  collapse = TRUE
)
```

<!-- https://lbusett.netlify.app/post/building-a-website-with-pkgdown-a-short-guide/ -->

# Models for Binary Responses

Consider first the Bernoulli distribution. For this, there 
are two possible outcomes,
e..g. 0 and 1, or success and failure.  For tosses of a fair
coin, the probability of a Head is $\frac{1}{2}$.  For
tosses of an unbiased die, the probability of a six is $\frac{1}{6}$.

## The Binomial and Poisson --- R functions

The binomial distribution, with size $n$, is the distribution
of the total number of 1's (or 'successes') in $n$ independent
Bernoulli trials with the same probability $\pi$.
On the assumption that heads appear independently between coin
tosses, with probability $\pi$ = 0.5 at each toss, the total
number of heads in 10 tosses is binomial with size $n$ = 10
and $\pi$ = 0.5.  The mean is $n \pi = 5$, while the variance is
$n \pi (1-\pi) = 2.5$ .

There is no necessary reason why Bernoulli trials must be
independent, or why the probability should be the same for
all trials.  As an example, if insects are exposed to a
fumigant that is not inevitably fatal, it is unlikely that 
the probability of death will be the same for all insects.
This has led to interest in other distributions,
able to model a wider variety of types of data.  In this
vignette, the primary concern is with such alternatives.

Event processes lead to the Poisson distribution and its
generalization.  For an
event process (e.g., radioactive decay events), the number
of counts in any time interval will be Poisson if:  

-  Events occur independently -- the occurrence of one event
    does not change the probability of a further event, and  
-  The rate $\lambda$ at which events occur is constant.

  Thus, in radioactive decay, atoms appear to decay independently
  (and emit ionizing radiation), at a rate that is the same for
  all atoms.  The mean is $\lambda$, which is also the variance.
  If the sample mean and the sample variance differ only by
  statistical error, data are to this extent consistent with a
  Poisson distribution.

Note that the Poisson distribution with rate $\lambda$
is the limiting distribution of the number of events
(counting 1 or "success" as an event) for a binomial
distribution as $n$ goes to infinity and $\pi$ goes to zero,
with the binomial mean constant at $n \pi = \lambda$. It is,
then, a limiting case of the binomial distribution.

Both for the binomial and for the Poisson, one parameter
determines both the mean and the variance.  This limits the 
data for which they can provide useful models, so that it
becomes necessary to look for alternatives. The most commonly 
implemented alternative to the binomial is the betabinomial.
There are a number of alternatives to the Poisson that have 
been widely implemented, with the negative  binomial is the 
best known.  See @rigby2019distributions for details of 
distributions that are implemented in the `gamlss` package.

For each distribution, there are four functions, with names
whose first letter is one of `d` (**d**ensity or,
for discrete distributions, probability),
`p` (cumulative **p**robability),
`q` (**q**uantile), and `r` (generate a
**r**andom sample).  For an example, look ahead to
the discussion of the binomial distribution in the next
subsection.  The following, using functions in the `stats` 
package that is by default available in R sessions, 
demonstrates the `d`/`p`/`q`/`r` nomenclature for the use
of the binomial distribution to model the probability of 
0, 1, 2, or 3 heads, in (`size`) 3 tosses, with probability 
(`prob` = 0.5) of a head.  The functions `dbinom()` and
`pbinom()` take number $x$ of `heads` as their first
argument, and return (for `dbinom()`) a probability
or (for `pbinom()`) a cumulative probability:
```{r binom-dp, echo=FALSE}
```
Observe the different names, in the two cases, for the argument 
that gives the number of `successes.

The inverse function to `pbinom()` is `qbinom()`.
This takes as first argument a cumulative probability `p`, 
and returns the smallest value of $x$ such that 
`pbinom(x)` $\leq$ `p`.
Thus it returns:

* 0 for any $p \leq 0.125$
* 1 for $0.125 < p \leq 0.5$
* 2 for $0.5 < p \leq 0.875$
* 3 for $0.875 < p \leq 1.0$

In particular:
```{r binom-q, echo=FALSE}
```
Figure \@ref(fig:plot-gph12) shows the (cumulative) distribution
function, with the quantile function alongside:

```{r cap1, echo=FALSE}
cap1 <- "Panel A shows the (cumulative) distribution function
for the binomial.  Panel B shows the inverse function, i.e.,
it plots the quantiles."
```

```{r binom-gph12, ref.label=c('binom-gph1','binom-gph2'), echo=FALSE}
``` 

```{r plot-gph12, fig.width=6.5, fig.height=2.75, out.width='90%', fig.show='hold', echo=FALSE, fig.cap=cap1, echo=FALSE}
plot(gph1, position=c(0,0,0.485,1), more=TRUE)
plot(gph2, position=c(.515,0,1,1), more=TRUE)
```

We would like to compare quantiles of a fitted
distribution with quantiles of the data, as a mechanism for
checking whether a model is a good fit to the data.  For
this, we prefer quantiles to be on a continuous dispersion.
A complication, for the present data, and for other such
discrete data, is that each of the horizontal lines in 
Figure \@ref(fig:plot-gph12)B corresponds to a range of 
probabilities, thus:
```{r quantile, results='asis', echo=FALSE}
```

Functions such as `qbinom()` return the point that,
moving from the vertical to the horizontal scale, is at 
the upper end of the relevant horizontal scale.
Quantiles that incorporate a random point along the
relevant horizontal line are preferable for use in model
diagnostics, preferably repeating any check with several 
different sets of such ``randomized quantile residuals.''
Depending on the number $x$ of diseased plants,
choose $u$ to be a uniform random number in the
corresponding interval shown above.  If $k=0$, choose
$u$ to be a uniform random number in the interval [0,0.125],
and so on.

Then, if the model is correct, $u$ will be uniformly
distributed on the interval $0 <= u <=1$.  Given a
set of values $u_i$, with $i=1, \ldots m$, these might be plotted
against quantiles of the uniform distribution on the
unit interval.  It is, however, usual to transform
to a standard normal distribution quantile scale.
Thus 0.2 will translate to -0.84, 0.5 to 0, and 0.8
to 0.84.
```{r u2q, echo=TRUE}
```

Thus transformed, the quantiles can be plotted against
quantiles of the normal distribution. Where a model
has been fitted, the process is applied at each fitted
value, generating "randomized quantile residuals."  The
"randomized quantile residuals" are residuals, on a
normal quantile scale, from the median of the fitted
distribution.

# More General Models for Binomial-like Counts 

The `glm()` function in the `stats` package allows 
`quasibinomial` (and `quasipoisson`)
families. This is not a formally defined distribution.
Instead, they fit just as for the `binomial` or `poisson`,
but estimate a `dispersion` from the fitted model
that allows the variance to be larger (or, possibly, smaller)
than the respective binomial or poisson variance.  

The `gamlss` package implements, in each case, several
alternatives.  Parameters follow naming conventions that 
are different from those used for the `stats` package's 
binomial family functions. The location parameter `prob`
becomes `mu` for `gamlss` functions, while  `size` 
becomes `bd` (= 'bound').  The `gamlss.dist` package has 
an accompanying pdf "The gamlss.family distributions" 
that describes the distributions that the package makes 
available.  

Bernoulli trials may not be independent, and/or the
probability may change from one trial to the next.
This is a common situation, which has not had the 
attention that it merits in the scientific literature.
In contexts where it is thought plausible that
the variance is a constant multiple $\Phi$
of the binomial variance, use of the `quasibinomial`
model fitting strategy has been common.
Quasibinomial fits proceed by fitting a binomial 
distribution, then multiplying the variance by a
'dispersion' factor $\Phi$ that is estimated from 
the data.  With $\Phi$ thus defined (and in this
context termed the 'dispersion'), the variance 
for the number $x$ of `successes' out of $n$ is 
$n \pi (1-\pi) \Phi$.  

Alternatives to the binomial that are implemented in the
`gamlss` package are the betabinomial and the double binomial.
These both have the binomial ($\Phi = 1$) as a limiting case.
In the `gamlss` implementation, the parameters are `mu` and 
`sigma`, while the `glmmTMB` betabinomial implementation
has `mu` and `phi`, where $\phi$ = $\sigma^{-1}$.  Both 
`sigma` and `phi` are described as "dispersion parameters" 
that, together with `mu` ( = $\pi$), determine the variance.
The relationship to the variance is in general different 
for different distributional families. 

In addition, there are zero-inflated versions of all the
distributions noted, and zero-adjusted versions 
of all except the double binomial. These have a further 
parameter, named `nu` in the `gamlss` implementation, that
is described as a ‘shape’ parameter.

## Notational conventions

An insightful way to
relate the different parameterizations of the betabinomial
is to express the dispersion parameter as a function of
the intra-class correlation $\rho$.  A positive correlation
leads to more homogeneous responses within replicates, and
manifests itself in greater between replicate differences,
leading to a dispersion index $\Phi$ that is greater than
one.  Then:
$$
\begin{aligned}
\rho &= \dfrac{\sigma}{\sigma+1} \quad \mbox{(}\sigma 
\mbox{ is the dispersion parameter in gamlss)}\\
&= \dfrac{1}{\phi+1} \quad \mbox{(}\phi 
\mbox{ is the dispersion parameter in glmmTMB)}
\end{aligned}
$$
The dispersion index (multiplier for $n \pi(\pi-1)$) is then
$$
\Phi = 1 + (n-1) \rho = \dfrac{1+ n \sigma}{1 + \sigma}
= \dfrac{\phi + n}{\phi + 1}
$$
I am not aware of any such simple formulae for the double 
binomial.  The double binomial allows for dispersion indices
that can be less than as well as greater than one.
For values of `sigma` for the double binomial, that are between 0.1
and 2.8, for $n$ = 10 and $\pi$ = 0.5, `sigma` never 
differs from the multiplier $\Phi$ for the binomial variance 
by more than 2%. 

The betabinomial can be implemented in a manner 
that allows an intra-class correlation $\rho$ that is
somewhat less than 0, and hence a dispersion index 
$\Phi$ that is somewhat less than one.  See
@prentice1986binary. However, most implementations
require $\Phi >= 1$ or (as for __glmmTMB__) $\Phi > 1$.  

```{r wtd-var, echo=FALSE}
```

<!-- ```{r alt, echo=FALSE, eval=FALSE} -->
<!-- ``` -->

For the betabinomial, again taking $\Phi$ to be the 
multiplier for the binomial variance:
\[
\sigma = \frac{\Phi-1}{n - \Phi}; \qquad \Phi = 1 + \frac{(n-1)\sigma}{\sigma+1}
\]



Figure `r if(PKGgamlss) paste("\\@ref(fig:cfDBI-BB)")` compares the binomial distribution 
with `size` $n$ =10, and probability $\pi$ = 0.5, with the 
dispersion parameter in each case chosen so that the dispersion 
factor is $\Phi = 2.0$ in Panel A, and $\Phi = 4.75$ in Panel B.

```{r gamlss-check, echo=FALSE, eval=nogamlss}
message("Subsequent code that requires `gamlss` will not be executed.")
```

```{r cap2, echo=FALSE}
cap2 <- "Panel A compares the double binomial (DBI) and the 
betabinomial (BB) distribution with the dispersion parameter
(DBI: $\\sigma = 2.0$; BB: $\\sigma = 0.125$) in each case chosen 
so that the dispersion index is $\\Phi = 2.0$. Panel B repeats
the comparison with the dispersion parameters (DBI: $\\sigma = 8.27$; 
BB: $\\sigma = \\frac{5}{7}$) chosen so that $\\Phi = 4.75$.
The binomial distribution ($\\Phi$ = 1), is shown for comparison, 
in both panels.  As $\\Phi$ increases, the change in shape needed
to accomodate the increased variance becomes more extreme."  
```

```{r cfDBI-BB, fig.width=9, fig.height=4, echo=FALSE, out.width='95%', fig.show='hold', fig.cap=cap2, eval=PKGgamlss}
```

Note also the correlated binomial, implemented in the 
`fitODBOD` package, but with limited functionality 
for working with the fitted model. It is noted here in 
order to reinforce the point that there are multiple 
alternatives to the betabinomial. 

### The Pólya urn model as motivation for the betabinomial

The betabinomial is a generalization, allowing continuous
parameter values, of the Pólya urn model.  An urn holds 
$\alpha$ red balls and $\beta$ blue balls.  In $n$ 
(= `size` or `bd`) draws, each ball that is withdrawn is
replaced, prior to the next draw, by two balls of the same 
color.  The effect is to move probability away from
the mean or mode, and towards the extremes.
See https://en.wikipedia.org/wiki/P%C3%B3lya_urn_model

### Comparing the betabinomial with the quasibinomial

R's `glm()` function offers the option of a quasibinomial error.
Specification of a quasibinomial error has the consequence that
the model is fitted as for a binomial distribution, with the
the binomial variance $n \pi (1- \pi)$ then multiplied by a
constant factor $\Phi$ that is usually estimated using the
Pearson chi-squared statistic.  For the betabinomial, the multiplier
is $\Phi = 1+(n-1)\rho$, i.e., it increases with $n$.  This is an
important difference.

Figure `r if(PKGgamlss) paste("\\@ref(fig:cfDBI-BB)")` 
highlighted the extent to which the
assumption of a distribution that has a binomial-like shape
will be seriously wrong, if the dispersion index $\Phi$ is
substantially greater than one.  Whatever the distribution,
if $\Phi >> 1$, the probability is pushed out 
towards the extremes, in ways that are sufficient to multiply 
the variance by the relevant factor $\Phi$.

## Data that do, and do not, appear binomial

```{r binData, eval=TRUE, echo=FALSE}
```

```{r fitVSobs, echo=FALSE}
```

Figure \@ref(fig:plot-binAB) compares observed with fitted
binomial counts, for two datasets. The data in Figure 
\@ref(fig:plot-binAB)A appear close to binomial.  That in 
Figure \@ref(fig:plot-binAB)B shows clear differences from
the binomial.


```{r cap3, echo=FALSE}
cap3 <- "In both panels, the vertical black lines show fitted
values when a binomial distribution is fitted to the data.
The data in Panel A has a distribution that is close to binomial.
That in Panel B appears to deviate from binomial."
```

```{r binAB, echo=FALSE}
```

```{r plot-binAB, fig.width=8, fig.height=3.25, out.width='98%', fig.show='hold', echo=FALSE, fig.cap=cap3}
plot(gph1, position=c(0,0,0.485,1), more=TRUE)
plot(gph2, position=c(.515,0,1,1), more=FALSE)
```

The Panel A data are the numbers of heads in 200 sets of ten coin tosses:
```{r htab, echo=FALSE}
htab
```
The data can be obtained from the `testDriveR` package, thus
```{r GetbinData, eval=FALSE, echo=1:4}
```

The Panel B data are the numbers of diseased plants, out of 6, in 62 
field quadrats.

```{r tastab, echo=FALSE}
tastab
```
The data can be obtained from the `testDriveR` package, thus
```{r GetbinData, eval=FALSE, echo=5:6}
```

For data such as here, a poor fit to a binomial model is 
to be expected.  The probability of disease is likely to
vary from one quadrat to another, with some clustering
of diseased plants within quadrats. 

## Diseased plants data --- comparison of alternative models

The code that now follows was used to obtain binomial, double binomial,
and betabinomial fits for the data, stored in the data frame `diseased`,
that were used for Figure \@ref(fig:plot-binAB)B. 
The fit is in each case handled as a special case of fitting a 
regression model. The only parameter is a location parameter 
--- hence the `~1`.

```{r cfFits, message=FALSE, echo=TRUE, eval=PKGgamlss}
```

Now examine, and compare, diagnostic plots for the binomial
(Figure \@ref(fig:cfsim)), and for the betabinomial (Figure 
\@ref(fig:cfq2)) model. Each plot is based on six sets of
randomized quantile residuals (`howmany=6` is the default),
while the setting `plot.type="all"` for Panels A and B has
the effect that all points are shown (in gray), while the
medians are shown as solid black dots.
Panel B (a 'worm plot') is a detrended version of Panel A,
with the dashed curves marking out 95% confidence bounds.

Figure `r if(PKGgamlss) paste("\\@ref(fig:cfsim)")` shows 
diagnostic plots for a binomial distribution:

```{r cap4, echo=FALSE}
cap4 <- "Diagnostic plots of randomized quantile residuals
(identified as sample quantiles), for a _binomial model_
fitted to the plant disease data.
Panel B (a 'worm plot') is a detrended version of Panel A,
with the dotted curves marking out 95% confidence bounds."
```


```{r cfsim, out.width='98%', fig.width=7.0, fig.height=2.25, echo=FALSE, fig.show='hold', fig.cap=cap4, eval=PKGgamlss}
```

Figure `r if(PKGgamlss) paste("\\@ref(fig:cfq2)")` shows 
diagnostic plots for a betabinomial distribution:

```{r cap5, echo=FALSE}
cap5 <- "Diagnostic plots of randomized quantile residuals, for
a __betabinomial__ model fitted to the plant disease data."
```

```{r cfq2,  out.width='98%', fig.width=7.0, fig.height=2.25, echo=FALSE, fig.show='hold', fig.cap=cap5, eval=PKGgamlss}
```

`r if(PKGgamlss) paste("\\@ref(fig:cfq2)")`
The worm plots give the clearest picture.  Figure 
`r if(PKGgamlss) paste("\\@ref(fig:cfsim)B")` makes it clear 
that the data is not binomial, while Figure
`r if(PKGgamlss) paste("\\@ref(fig:cfq2)B")` indicates 
that the data are broadly consistent with a betabinomial 
distribution. 

Code is:
```{r cfsim, eval=FALSE, echo=c(2,3,5,7), ref.label=c('cfsim','cfq2')}
```

Replace `doBI` by `doBB`, for the code for the betabinomial diagnostic 
plots.

<!-- \noindent Data  Code is: -->
<!-- ```{r cfq2, eval=FALSE, echo=c(2,3,5,7)} -->
<!-- ``` -->

<!-- ```{r DBI-CBin, eval=1:2, echo=1:2} -->
<!-- ``` -->

The AIC statistic can be used for a theoretically based
comparison between model fits.  As with other such
'information' statistics, the AIC statistic is not 
designed to provide statistical tests.  The following 
uses the AIC statistic to compare the three models that
have been fitted -- the binomial, the betabinomial,
and the double binomial (for which diagnostic plots
have not been shown.) The values given in the `dAIC`
column are increases from the best fitting model.
```{r cf-AIC, eval=PKGgamlss}
```

The comparison favors the betabinomial, by a smallish margin.

### Do differences from the binomial matter? {-}

Do the differences matter?  That depends on the use that will be made
of the results.  If the use of the model depends on accurately
predicting the proportion of diseased samples, the differences
clearly will matter.
A confidence interval, e.g., for a difference between two different
sample areas, may be acceptably accurate provided that normal
approximations are used for the sampling distributions of the
two means, and the variances are calculated from the data.  For this,
we are relying on Central Limit Theorem effects (see ??) to bring
the sampling distribution of the mean close to the normal.

## Numbers of males, in first 12 of 13 children

Data from large families indicates that variation in the
proportion of males is greater than would be expected for a
binomial distribution.  The dataset `qra::malesINfirst12`,
from hospital records in Saxony in the nineteenth century, gives
the number of males among the first 12 children of family size 13
in 6115 families. The probability that a child will be male
varies, within and/or between families.
(The 13th child is ignored to counter the effect of families
non-randomly stopping when a desired gender is reached.) Data is:

```{r Saxonymales}
```

The following fits the model with binomial errors.

```{r cfMales, echo=1:2, eval=PKGgamlss}
```
For the betabinomial (`doBB`), replace `family=BI` by `family=BB`. 
For the double binomial (`doDBI`), replace `family=BI` by `family=DBI`. 

Fitted probabilities for the betabinomial can, if required, be 
calculated and added to the data frame `maleFit` thus:

```{r binFitProbs, echo=1:3, eval=PKGgamlss}
```

The code can be modified in the obvious way to add fitted values
for the binomial and/or the double binomial.

```{r cap6, echo=FALSE}
cap6 <- "Data, from hospital records in Saxony in the nineteenth century, gave the number of males among the first 12 children in families of size 13, in 6115 families. Panel A shows a worm plot for the model that fitted a binomial distribution.  Panel B repeats the worm plot, now for the model that fitted a betabinomial distribution."
```

Figure `r if(PKGgamlss) paste("\\@ref(fig:rqmales)")` shows worm 
plots for the binomial and betabinomial models:

```{r rqmales, out.width='85%', fig.width=8.4, fig.height=3.1, echo=FALSE,  fig.show='hold', fig.cap=cap6, eval=PKGgamlss}
```

The Panel A plot, with points all lying close to a line, indicates
that data follow a binomial-like pattern of variation.  The upward
slope of the line indicates that points are more dispersed than for
a binomial distribution.  The Panel B plot indicates that data are
consistent with the betabinomial fit from which this plot was
generated.

\noindent Code for the plots is:
```{r rqmales, eval=FALSE, echo=c(2,4)}
```

AIC statistics for the three models are:

```{r cf-AIC-males, echo=FALSE, eval=PKGgamlss}
```
The double binomial is by a small and inconsequential
margin the preferred model.

### Bernoulli 0/1 data are a special case {-}

Note that for data with a 0/1 response, neither the
`quasibinomial` nor the `betabinomial` or other such
model can be fitted, unless the 0/1 responses can be
grouped to give repeated $x$ out of $n$ sets of results.
With a 0/1 response, the residual deviance is a function
of the fitted parameters, and gives no information on
the variance. See @McCullagh, Section 4.5.1.

# Count data --- Poisson and Related Distributions

We now move to examine models for count data, initially
fitting Poisson distributions.

## Data that do, and do not, appear Poisson

Figure \@ref(fig:plot-poisAB) compares observed counts with fitted
poisson counts, for two datasets.

```{r countdata, eval=TRUE, echo=FALSE}
```
The first set of counts are the @rutherford1910lxxvi polonium radioactive decay counts that give the number of scintillations in 2608 1/8 minute intervals.

```{r ruge, echo=F}
```

The second set of counts are numbers of accidents among 414 machinists
from a three months study conducted around the end of WWI
[@greenwood1919incidence; see also @greenwood1920inquiry]:
```{r accs, echo=F}
```

The following code calculates the means, for the two datasets, then
calculating fitted values for fits to the Poisson distribution
```{r countFits, eval=TRUE, echo=TRUE}
```

```{r cap7, echo=FALSE}
cap7 <- "In both panels, the vertical black lines show fitted
values when a Poisson distribution is fitted to the data.
The data in Panel A has a distribution that appears close to poisson.
That in Panel B appears to deviate from Poisson.  Notice that the
variance for the poisson fit (`r round(mumach,2)`) is smaller than 
the variance that is estimated from the data (1.01) by a factor of 
a little more than 2."
```

```{r poisAB, echo=FALSE}
```

```{r plot-poisAB, fig.width=8, fig.height=3.25, out.width='98%', fig.show='hold', fig.cap=cap7, echo=FALSE}
plot(gph1, position=c(0,0,0.485,1), more=TRUE)
plot(gph2, position=c(.515,0,1,1), more=FALSE)
```
In Panel A of Figure \@ref(fig:plot-poisAB), the data appear 
consistent with a Poisson distribution.  In Panel B, the 
Poisson fit underestimates the number of zeros, and overestimates
the number of ones.  In principle, the underestimation of the
number of zeros can be fixed by fitting a zero-inflated Poisson
distribution.  It turns out, that while this improves the fit,
the fit is still less than satisfactory.  The accident risk is
likely to vary between machinists, invalidating the constant rate
assumption required for a Poisson distribution.

## Models for Poisson-Like Counts

Among alternatives to the Poisson that allow for departures
from the Poisson assumptions of constant rate $\lambda$ and
independence between the events that are counted, the
negative binomial has been the most widely used, in part
because it was for a time the only alternative that was
widely implemented.   The `gamlss` and other R packages 
now implement a number of alternatives that can be used
with count data.

```{r cfFits-poiss, message=FALSE, eval=TRUE, echo=c(1:2,10:13)}
```
Modify `family=poisson` as required for `doNBI` (`family=NBI`),
`doPIG` (`family=PIG`), and `doZIP` (`family=ZIP`)

Now compare the AIC statistic between these three models:
```{r cf-AIC-counts, eval=TRUE, echo=FALSE}
```

The zero-inflated Poisson does do a better job than the
Poisson, but is much less satisfactory than a negative
binomial or a Poisson inverse gamma model.  As there is
little to choose between the negative binomial and the 
Poisson inverse gamma model, the more widely implemented
negative binomial fit is likely to be preferred.

```{r cap8, echo=FALSE}
cap8 <- "Worm plots for the Poisson, for the zero-inflated Poisson,
and for the negative binomial."
```

```{r wormpois, out.width='99%', fig.width=8.0, fig.height=2.5, echo=FALSE, fig.show='hold', fig.cap=cap8, eval=PKGgamlss}
```

The following demonstrates the calculation of the fitted frequencies
for the negative binomial, Poisson inverse gamma, and zero inflated
Poisson fits.  The zero inflated Poisson ensures that the estimated
number of zeros exactly equals the observed number, but gets the
relative numbers of frequencies 1, 2 and 3 badly wrong.

```{r EstFreqs}
```

# References {-}

```{r exit, echo=FALSE}
options(oldopt)
knitr::knit_exit()
```