Description Usage Arguments Details Value Warning Author(s) See Also Examples
Compare nested models inheriting from class "BTm"
. For models with no
random effects, compute analysis of deviance table, otherwise compute Wald
tests of additional terms.
1 2 
object 
a fitted object of class inheriting from 
... 
additional 
dispersion 
a value for the dispersion. Not implemented for models with random effects. 
test 
optional character string (partially) matching one of

For models with no random effects, an analysis of deviance table is computed
using anova.glm()
. Otherwise, Wald tests are computed as
detailed here.
If a single object is specified, terms are added sequentially and a Wald
statistic is computed for the extra parameters. If the full model includes
player covariates and there are players with missing values over these
covariates, then the NULL
model will include a separate ability for
these players. If there are missing values in any contestlevel variables in
the full model, the corresponding contests will be omitted throughout. The
random effects structure of the full model is assumed for all submodels.
For a list of objects, consecutive pairs of models are compared by computing a Wald statistic for the extra parameters in the larger of the two models.
The Wald statistic is always based on the variancecovariance matrix of the larger of the two models being compared.
An object of class "anova"
inheriting from class
"data.frame"
.
The comparison between two or more models will only be
valid if they are fitted to the same dataset. This may be a problem if there
are missing values and 's default of na.action = na.omit
is used. An
error will be returned in this case.
The same problem will occur when separate abilities have been estimated for different subsets of players in the models being compared. However no warning is given in this case.
Heather Turner
1 2 3 4 5  result < rep(1, nrow(flatlizards$contests))
BTmodel < BTm(result, winner, loser, ~ throat.PC1[..] + throat.PC3[..] +
head.length[..] + (1..), data = flatlizards,
trace = TRUE)
anova(BTmodel)

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