chameleons {BradleyTerry2} | R Documentation |

## Male Cape Dwarf Chameleons: Measured Traits and Contest Outcomes

### Description

Data as used in the study by Stuart-Fox et al. (2006). Physical measurements made on 35 male Cape dwarf chameleons, and the results of 106 inter-male contests.

### Usage

```
chameleons
```

### Format

A list containing three data frames: `chameleons$winner`

,
`chameleons$loser`

and `chameleons$predictors`

.

The `chameleons$winner`

and `chameleons$loser`

data frames each
have 106 observations (one per contest) on the following 4 variables:

- ID
a factor with 35 levels

`C01`

,`C02`

, ... ,`C43`

, the identity of the winning (or losing) male in each contest- prev.wins.1
integer (values 0 or 1), did the winner/loser of this contest win in an immediately previous contest?

- prev.wins.2
integer (values 0, 1 or 2), how many of his (maximum) previous 2 contests did each male win?

- prev.wins.all
integer, how many previous contests has each male won?

The `chameleons$predictors`

data frame has 35 observations, one for
each male involved in the contests, on the following 7 variables:

- ch.res
numeric, residuals of casque height regression on

`SVL`

, i.e. relative height of the bony part on the top of the chameleons' heads- jl.res
numeric, residuals of jaw length regression on

`SVL`

- tl.res
numeric, residuals of tail length regression on

`SVL`

- mass.res
numeric, residuals of body mass regression on

`SVL`

(body condition)- SVL
numeric, snout-vent length (body size)

- prop.main
numeric, proportion (arcsin transformed) of area of the flank occupied by the main pink patch on the flank

- prop.patch
numeric, proportion (arcsin transformed) of area of the flank occupied by the entire flank patch

### Details

The published paper mentions 107 contests, but only 106 contests are included here. Contest number 16 was deleted from the data used to fit the models, because it involved a male whose predictor-variables were incomplete (and it was the only contest involving that lizard, so it is uninformative).

### Author(s)

David Firth

### Source

The data were obtained by Dr Devi Stuart-Fox, https://devistuartfox.com/, and they are reproduced here with her kind permission.

These are the same data that were used in

Stuart-Fox, D. M., Firth, D., Moussalli, A. and Whiting, M. J. (2006)
Multiple signals in chameleon contests: designing and analysing animal
contests as a tournament. *Animal Behaviour* **71**, 1263–1271.

### Examples

```
##
## Reproduce Table 3 from page 1268 of the above paper:
##
summary(chameleon.model <- BTm(player1 = winner, player2 = loser,
formula = ~ prev.wins.2 + ch.res[ID] + prop.main[ID] + (1|ID), id = "ID",
data = chameleons))
head(BTabilities(chameleon.model))
##
## Note that, although a per-chameleon random effect is specified as in the
## above [the term "+ (1|ID)"], the estimated variance for that random
## effect turns out to be zero in this case. The "prior experience"
## effect ["+ prev.wins.2"] in this analysis has explained most of the
## variation, leaving little for the ID-specific predictors to do.
## Despite that, two of the ID-specific predictors do emerge as
## significant.
##
## Test whether any of the other ID-specific predictors has an effect:
##
add1(chameleon.model, ~ . + jl.res[ID] + tl.res[ID] + mass.res[ID] +
SVL[ID] + prop.patch[ID])
```

*BradleyTerry2*version 1.1-2 Index]