---
title: "Quantifying the rock-paper-scissors effect"
author: "R. K. S. Hankin"
output: bookdown::html_document2
bibliography: hyper2.bib  # copy vignettes/hyper2.bib to inst/
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library("hyper2")
library("magrittr")
abbreviated <- TRUE  # change to FALSE for full names
```

```{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;
takes about an hour to run without cache.  Here I use the `hyper2`
package to quantify the strength of a circular rock-paper-scissors
effect using the `hyper2` package, improving on previous results
[@west2008] which merely established its existence.  I use two
datasets, a three-player scoreline and a synthetic dataset referring
to wear on a seven-tooth gear wheel.

## Rock Paper Scissors

Consider the following dataset:

```{r,chesstable}
chess_table <- as.matrix(read.table("chess.txt"))
dimnames(chess_table) <- list(
    match=c("T_vs_A","A_vs_K","T_vs_K"),
    player=c("Topalov","Anand","Karpov"))
chess_table
```

This is documented in `chess.Rd` and shows that Topalov played Anand
$22+13=35$ times, winning 22 and losing 13 matches.  The corresponding
likelihood function is `chess`:

```{r,showchess}
chess <- saffy(chess_table)
chess
chess_maxp <- maxp(chess)
chess_maxp
```

(we refer to this as the "TAK" model).  We might ask whether there is
evidence for the players having differing strengths:

```{r equalptestchunk,cache=TRUE}
equalp.test(chess)
```

Thus we fail to reject $H_0\colon p_T=p_A=p_K$ under T-A-K as the
p-value is about 0.39.

## RPS in chess

The existence of a rock-paper-scissors relationship between the
players was discussed in @hankin2010 and the null of no RPS phenomena
could be rejected using the Alymer test.

Here we improve on that work by introducing a monster, `M`, that
accounts for the rock-paper-scissors nature of the players.  The RPS
monster helps players that benefit from the RPS nature of the result.
The simplest nontrivial probability model is that all three real
players have the same strength: $p_T=p_A=p_K$, which we write $T=A=K$.
Under $T=A=K$, writing $p_T=p_A=p_K=\alpha$ we can use a binomial test
to calculate the p-value for the hypothesis that $p_M=0$:

```{r,binomtest}
binom.test(sum(diag(chess_table)),sum(chess_table,na.rm=TRUE),alternative="greater")
```

Estimating $p_M$ is also straightforward:

$$
Prob(\mathrm{procyclic}\ \mathrm{win})=\frac{\alpha+M}{2\alpha+M}
$$


(a "procyclic win" is a win that would be expected under a strong
monster).  Here we also have $3\alpha+M=1$, so to estimate the
strength of the monster we would solve

$$
\frac{\alpha+M}{2\alpha+M}=p,\qquad{3\alpha + M}=1
$$

for $\alpha$ and $M$, where $p$ is the probability of a procyclic win.
This gives us

$$
\alpha=\frac{1-p}{2-p},\qquad M=\frac{2p-1}{2-p}.
$$

If we use the maximum likelihood estimate value for a procyclic win of
$\frac{55}{88}=0.625$ we find that $\hat{M}=0.18$ approximately.
Further, we may express $p$, the probability of a pro-cyclic win, as
$p=\frac{1+2M}{2+M}$.  If we observe $a$ procyclic wins and $b$
countercyclic wins a likelihood function for $M$ would then be

$$
\mathcal{L}(M)= p^a(1-p)^b=\left(\frac{1+2M}{2+M}\right)^a\cdot
\left(\frac{1-M}{2+M}\right)^b
$$

and here we have $a=22+23+10=55$ and $b=13+12+8=33$ (a graph is given
below).  It is straightforward but not trivial to translate this into
`hyper3` idiom.  Noting that $3\alpha+M=1$ we define $\beta=3\alpha$
and then the unit sum constraint is $\beta+M=1$ and our probabilities
are

$$
\frac{\alpha+M;\alpha}{2\alpha+M   } =
\frac{\beta/3+M;\beta/3}{2\beta/3+M} =
\frac{1+2M;1-M}{2+M}
$$

(where the last expression is obtained using $\beta+M=1$).  We may set
up a log-likelihood function of $a,b$ using `hyper3` idiom as follows:

```{r,usehyper3}
TqAqK <- function(a,b){
  H3 <- hyper3(pnames=c("beta","M"))
  H3[c(beta=1/3,M=1)] %<>% inc(a  )
  H3[c(beta=1/3    )] %<>% inc(  b)
  H3[c(beta=2/3,M=1)] %<>% dec(a+b)
  return(H3)
}
H3 <- TqAqK(55,33)
loglik(c(M=0.1,beta=0.9),H3)
loglik(c(beta=0.9,M=0.1),H3)  #  NB: identical!
```

[above, we note that $\beta+M=3\alpha+M=1$, so the monster is the
fillup value with respect to $\beta$].  This is easy to maximize:

```{r,maximizeTqAqK}
optimize(function(M){loglik(c(M=M,beta=1-M),H3)},c(0.01,0.99),maximum=TRUE)
```

This gives maximum likelihood estimates of $M=0.18$ as would be
expected from the simple binomial model; the probability of a
procyclic win is about $0.625$.

We may relax the assumption of equal strength of the real
players by using a more sophisticated hyper2 construction:

```{r,setupH}
H <- hyper2()
H[c("Topalov",                 "M")] %<>% inc(22   )
H[c(          "Anand"             )] %<>% inc(   13)
H[c("Topalov","Anand",         "M")] %<>% dec(22+13)

H[c(          "Anand",         "M")] %<>% inc(   23)
H[c(                  "Karpov"    )] %<>% inc(   12)
H[c(          "Anand","Karpov","M")] %<>% dec(23+12)

H[c("Topalov"                     )] %<>% inc( 8   )
H[c(                  "Karpov","M")] %<>% inc(   10)
H[c("Topalov",        "Karpov","M")] %<>% dec( 8+10)
H
```

(in the above, a win by a player helped by the RPS effect has `M`
added to his strength).  We can call this the TAKM model, and find the
evaluate:

```{r,calcmaxp,cache=TRUE}
maxp(H)
```

We see that, under TAKM, the monster has strength of about 0.16,
slightly less than the 0.18 obtained for $M$ under the equality
assumption $p_T=p_A=p_K$.  Further, under TAKM, the three "real"
players are again all of approximately equal strength, and we may test
this:

```{r,testequalTAKM,cache=TRUE}
samep.test(H,c("Topalov","Anand","Karpov"))
```

(note the estimated monster strengths of 0.16 and 0.18 agree with the
previous estimate).  Thus the p-value of about 0.77 indicates failure
to reject the null (compare 0.39 under TAK).  Now we can test
$H_0\colon p_M=0$ under TAKM:

```{r,specmtest,cache=TRUE}
specificp.gt.test(H,"M",0)
```

(in the test above, note that the players may appear in a different
order from previously).  The test shows that we may reject the
hypothesis that $p_M=0$ under TAKM with a p-value of $0.04$ (compare
0.012 from the binomial test).


### Profile likelihood

We may plot a profile likelihood curve for $M$:

```{r,label=plotproflik,cache=TRUE}
M <- seq(from=0.001,to=0.4,len=40)
L1 <- profsupp(H,"M",M)
L1 <- L1-max(L1)

L2 <- 55*log(1+2*M)+33*log(1-M)-88*log(2+M)
L2 <- L2-max(L2)

plot(M,L1,type="l",col="black",ylab="log likelihood",xlab="Monster strength")
points(M,L2,type="l",col="red")
abline(h=c(0,-2),lty=2)
legend("topright",col=c("black","red"),lty=1,legend=c("TAKM","T=A=K"))
```

Above we see support curves for RPS monster strength using TAKM and
$T=A=K$, showing very similar results.  Note that the T=A=K curve
could have been obtained more nicely with function `TqAqK` but I
haven't got round to changing it yet.

# Generalized RPS

West and Hankin (2008) consider a synthetic dataset referring to seven
teeth of a gear wheel.  Consecutive pairs are repeatedly compared and
the most worn tooth is nominated by independent judges.  Here a
dataset of the same form will be used, but the entries have been
changed for pedagogical reasons:

```{r,echo=FALSE}
t1 <- "t1"
t2 <- "t2"
t3 <- "t3"
t4 <- "t4"
t5 <- "t5"
t6 <- "t6"
t7 <- "t7"
M <- "M"
```

```{r,label=setuphw}
hw <- matrix(c(
    16,05,NA,NA,NA,NA,NA,
    NA,06,09,NA,NA,NA,NA,
    NA,NA,05,11,NA,NA,NA,
    NA,NA,NA,06,12,NA,NA,
    NA,NA,NA,NA,05,09,NA,
    NA,NA,NA,NA,NA,04,07,
    13,NA,NA,NA,NA,NA,05),
    7,7,byrow=TRUE)
colnames(hw) <- paste("t",1:7,sep="")
hw
```

Thus, considering the top row, we see that in a total of $16+5=21$
independent comparisons between `t1` and `t2`, tooth `t1` was judged
to be the more worn one 16 times, and `t2` was judged to be the more
worn one $5$ times (NB: in the above, I have changed the dataset in
order to make `t1` stronger than the other players while maintaining
the RPS phenomenon).  We may convert this dataset to a generalized
`hyper2` object.  First a standard Bradley-Terry:

```{r,label=HWnomonster}
HW <- hyper2()
rs <- -rowSums(hw,na.rm=TRUE)
cs <- colSums(hw,na.rm=TRUE)
HW[t1] <- cs[1]
HW[t2] <- cs[2]
HW[t3] <- cs[3]
HW[t4] <- cs[4]
HW[t5] <- cs[5]
HW[t6] <- cs[6]
HW[t7] <- cs[7]
HW[c(t1,t2)] <- rs[1]
HW[c(t2,t3)] <- rs[2]
HW[c(t3,t4)] <- rs[3]
HW[c(t4,t5)] <- rs[4]
HW[c(t5,t6)] <- rs[5]
HW[c(t6,t7)] <- rs[6]
HW[c(t7,t1)] <- rs[7]
HW
```

(likelihood function `HW` has no monster).  We can then analyse this:

```{r,label=testHW,cache=TRUE}
maxp(HW)
equalp.test(HW)
```

Thus according to this model we may reject the hypothesis of equal
strength.  We now incorporate the monster:

```{r,label=NWwithmonster}
HWM <- hyper2()
tt <- c("t1","t2","t3","t4","t5","t6","t7")
for(i in 1:6){
  HWM[c(tt[i]          )] <-  hw[i,i  ]
  HWM[c(      tt[i+1],M)] <-  hw[i,i+1]
  HWM[c(tt[i],tt[i+1],M)] <- rs[i]
}
HWM[c(tt[7]        )] <-  hw[7,7]
HWM[c(      tt[1],M)] <-  hw[7,1]
HWM[c(tt[7],tt[1],M)] <- rs[7]
HWM
```

Then the same analysis applies:

```{r,label=maxpHWM,cache=TRUE}
maxHW <- maxp(HWM)
maxHW
```

and we can test the hypothesis that $p_M=0$:

```{r,label=specificgtesthwm,cache=TRUE}
specificp.gt.test(HWM,"M",0)
```

suggesting that we may reject the hypothesis of zero monster strength
with a p-value of about 4.7\%, even after accounting for the different
strengths of the teeth.

### Analysis of seven-tooth gear with `hyper3`

We can now use `hyper3` to analyse the same dataset.  Here the
likelihood function for pairwise competition between $p_1$ and $p_2$,
with $a$ procyclic wins and $b$ anticyclic wins, is

$$
\left(\frac{\lambda p_1}{\lambda p_1 + p_2}\right)^a
\cdot
\left(\frac{p_2}{\lambda p_1 + p_2}\right)^b
$$

where $\lambda$ is a quantification of the asymmetry.  We can modify
the `hyper2` idiom to make a `hyper3` object instead:

```{r,label=defineh3seven}
H3seven <- function(lambda){
  H3 <- hyper3()
  for(i in 1:6){
     jj <- 1  # diagonal players do not have lambda-advantage
     names(jj) <- tt[i]
     H3[jj] %<>% inc(hw[i,i])

     jj <- lambda
     names(jj) <- tt[i+1]
     H3[jj] %<>% inc(hw[i,i+1])

     jj <- c(1,lambda)
     names(jj) <- c(tt[i],tt[i+1])
     H3[jj] %<>% dec(hw[i,i] + hw[i,i+1])
  }  # i loop closes

  ## Now do final one:
  jj <- 1
  names(jj) <- tt[7]
  H3[jj] %<>% inc(hw[7,7])

  jj <- lambda
  names(jj) <- tt[1]
  H3[jj] %<>% inc(hw[7,1])

  jj <- c(1,lambda)
  names(jj) <- c(tt[7],tt[1])
  H3[jj] %<>% dec(hw[7,7] + hw[7,1])

  return(H3)
}  # function H3seven() defn closes
```


Now we can execute it:

```{r,label=executeh3seven}
H3 <- H3seven(lambda=1.66)
H3
```




```{r,label=defineproflikeh3}
profl <- function(lambda){
  H <- H3seven(lambda)
  return(loglik(maxp(H),H))
}
```

First optimize the likelihood:


```{r,optlike,cache=TRUE}
o <- optimize(profl,c(1.4,1.6),maximum=TRUE)
```

```{r}
o
```


So now plot a profile likelihood curve:


```{r,label=proflikh3,cache=TRUE}
lam <- seq(from=0.8,to=2.6,len=30)
like3 <- sapply(lam,profl)
```

```{r,plotproflike3}
plot(lam,like3-o$objective,type='b',ylab="likelihood",xlab="lambda")
abline(h=c(0,-2))
abline(v=c(1,o$maximum))
```


### Package dataset {-}

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

```{r,label=savechessdataset}
save(chess_table,chess,chess_maxp,file="chess.rda")
```


## References {-}





