---
title: "volleyball dataset"
author: "R. K. S. Hankin"
output: bookdown::html_document2
bibliography: hyper2.bib  # copy vignettes/hyper2.bib to inst/
---

```{r setup, include=FALSE}
set.seed(0)
knitr::opts_chunk$set(echo = TRUE)
library("hyper2")
library("magrittr")
options("digits" = 5)
```

```{r out.width='20%', out.extra='style="float:right; padding:10px"',echo=FALSE}
knitr::include_graphics(system.file("help/figures/hyper2.png", package = "hyper2"))
```

To cite the `hyper2` package in publications, please use @hankin2017_rmd.
This short document discusses a dataset first presented by @hankin2010,
although here only the first 52 observations are used.  A
volleyball _set_ is a Bernoulli trial between two disjoint subsets
of the players.  The two subsets are denoted (after the game) as the
_winners_ and the _losers_: these are denoted by `1` and `0`
respectively.

```{r readcurlingtable}
volleyball_table <- as.matrix(read.table("volleyball.txt",header=TRUE))
nrow(volleyball_table)
head(volleyball_table)
```

Each row of `volleyball_table` is a set.  Thus the first line shows a
game between a team comprising `p1`, `p4`, and `p8` against a team
comprising `p5` and `p6`.  Player `p9` did not play; team `p1 p4 p8`
won.  We may use function `volley()` to convert this to a likelihood
function:

```{r calcmaxpvolley,cache=TRUE}
volleyball <- volley(volleyball_table)
(volleyball_maxp <- maxp(volleyball))
```

The original synthetic dataset was prepared using Zipf's law for the
players' strengths, so we may test the hypothesis that this is the
case; $H_0\colon p_i\propto i^{-1}$:

```{r zipflikelihood}
zipf <- function(n){jj <- 1/(1:n); jj/sum(jj)}
zipf(9)
(null_support <- loglik(indep(zipf(9)),volleyball))
(alternative_support <- loglik(indep(volleyball_maxp),volleyball))
(Lambda <- 2*(alternative_support-null_support))
pchisq(Lambda,df=8,lower.tail=FALSE)
```

somewhat disappointingly rejecting the null with a $p$-value of about
4\% (and indeed--just---with a two units of support per degree of
freedom criterion).  However, it is not clear to me the extent to
which Wilks's theorem is applicable here [Wilks is an asymptotic
result; recall that we have only 52 observations here], nor whether
the support criterion is appropriate with 8 degrees of freedom.


### Package dataset {-}

Following lines create `volleyball.rda`, residing in the `data/` directory of the package.

```{r,label=savevolleyballdataset}
save(volleyball_table,volleyball_maxp,volleyball,file="volleyball.rda")
```


## References {-}