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.
A list containing three data frames:
chameleons$loser data frames each
have 106 observations (one per contest) on the following 4 variables:
a factor with 35 levels
C43, the identity of the winning (or losing) male in each
integer (values 0 or 1), did the winner/loser of this contest win in an immediately previous contest?
integer (values 0, 1 or 2), how many of his (maximum) previous 2 contests did each male win?
integer, how many previous contests has each male won?
chameleons$predictors data frame has 35 observations, one for
each male involved in the contests, on the following 7 variables:
numeric, residuals of casque height regression on
SVL, i.e. relative height of the bony part on the top of the
numeric, residuals of jaw length
numeric, residuals of tail
length regression on
of body mass regression on
SVL (body condition)
numeric, snout-vent length (body size)
numeric, proportion (arcsin transformed) of area of the flank occupied by the main pink patch on the flank
numeric, proportion (arcsin transformed) of area of the flank occupied by the entire flank patch
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).
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.
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## ## 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])
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