R: Male Cape Dwarf Chameleons: Measured Traits and Contest...

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, http://www.zoology.unimelb.edu.au/research/groups/animal/labs/stuart-fox/index.php, 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))
    ##
    ## 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])

[Package BradleyTerry2 version 1.0-6 Index]