---
title: "curling with hyper2 objects"
author: "R. K. S. Hankin"
output: bookdown::html_document2
bibliography: hyper2.bib  # copy from vignettes/

---

```{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.
[Curling](https://en.wikipedia.org/wiki/Curling) is a sport with
stones and brooms.  Following dataset taken from
[wikipedia](https://en.wikipedia.org/w/index.php?title=Curling_at_the_Winter_Olympics&oldid=998891075),
women's tournament:

```{r readcurlingtable}
curling_table  <- read.table("curling.txt",header=TRUE)
curling_table
```

Thus the first row shows that Canada came first in 1998, third in
2002, and so on; the first column shows that in 1998, Canada came
first, China did not compete [`NA` meaning did not compete], Denmark
came second, and so on.  First we will interpret `NA` in the same way
as `DNS` in the context of formula 1 motor racing.  We need to define
a new data frame, `b`:

```{r,defineb}
ct <- curling_table
ct[is.na(ct)] <- "DNS"
ct
```

In this form we can easily convert to a support function:

```{r,definsupportb,cache=TRUE}
curling1 <- ordertable2supp(ct)
head(curling1)
curling1_maxp <- maxp(curling1)
pie(curling1_maxp)
```

## Interpretation of `NA`

There is a different interpretation of `NA` entries.  We might hold
that no inference should be made from a team's entry being `NA` entry
because that that team did not compete.  This means that function
`ordertable2supp()` is not appropriate and we need to use a different
method:

```{r,makehyper2curling,cache=TRUE}
curling2 <- hyper2()

for(i in seq_len(ncol(curling_table))){
    x <- curling_table[,i]
    names(x) <- rownames(curling_table)
    curling2 <- curling2 + ordervec2supp(x[!is.na(x)])
}
head(curling2)
(curling2_maxp <- maxp(curling2))
pie(curling2_maxp)
```


##  Comparison

```{r,plotcomplog}
par(pty="s")
plot(log10(curling1_maxp),log10(curling2_maxp),asp=1,pch=16,xlim=c(-2.6,0),ylim=c(-2.6,0))
posn <- c(Canada=2, China=4, Denmark=4, Germany=4, GB=4, Italy=1, Japan=2, Norway=2,
          Russia=4, South_Korea=4, Sweden=4, Switzerland=2, US=4)
for(i in seq_len(nrow(curling_table))){
	text(log10(curling1_maxp)[i],log10(curling2_maxp)[i],rownames(curling_table)[i],col="gray",pos=posn[i])
}
abline(0,1)
grid()
```

The two likelihood systems have very different evaluates.


### Package dataset {-}

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

```{r,label=savecurlingdataset}
save(curling_table,curling1,curling2,curling1_maxp,curling2_maxp,file="curling.rda")
```

### References {-}

Curling at the Winter Olympics, https://en.wikipedia.org/w/index.php?title=Curling_at_the_Winter_Olympics&oldid=998891075 (last visited Jan. 21, 2021).




