score.test(secr, ..., betaindex = NULL, trace = FALSE, ncores = 1, .relStep = 0.001,
minAbsPar = 0.1)score.table(object, ..., sort = TRUE, dmax = 10)
fdHessfdHessIf … defines several alternative models then a list of score.test objects is returned.
The output from score.table is a dataframe with one row per model, including the reference model.
Score tests allow fast model selection (e.g. Catchpole & Morgan 1996).
Only the simpler model need be fitted. This implementation uses the
observed information matrix, which may sometimes mislead (Morgan et al.
2007). The gradient and second derivative of the likelihood function are
evaluated numerically at the point in the parameter space of the second
model corresponding to the fit of the first model. This operation uses
the function fdHess of the nlme package; the likelihood
must be evaluated several times, but many fewer times than would be
needed to fit the model. The score statistic is an approximation to the
likelihood ratio; this allows the difference in AIC to be estimated.
Covariates are inferred from components of the reference model
secr. If the new models require additional covariates these may
usually be added to the respective component of secr.
Mapping of parameters between the fitted and alternative models
sometimes requires user intervention via the betaindex argument.
For example betaindex = c(1,2,4) is the correct mapping when
comparing the null model (D\(\sim{~}\)1, g0\(\sim{~}\)1,
sigma\(\sim{~}\)1) to one with a behavioural effect on g0
(D\(\sim{~}\)1, g0\(\sim{~}\)b, sigma\(\sim{~}\)1).
The arguments .relStep and minAbsPar control the numerical
gradient calculation and are passed directly to
fdHess. More investigation is needed to determine
optimal settings.
score.table summarises one or more score tests in the form of a
model comparison table. The … argument here allows the inclusion of
additional score test objects (note the meaning differs from
score.test). Approximate AICc values are used to compute relative
AIC model weights for all models within dmax AICc units of the best
model.
Multiple cores provide some speed improvment in score.test when
comparing more than two models.
Hurvich, C. M. and Tsai, C. L. (1989) Regression and time series model selection in small samples. Biometrika 76, 297--307.
McCrea, R. S. and Morgan, B. J. T. (2011) Multistate mark-recapture model selection using score tests. Biometrics 67, 234--241.
Morgan, B. J. T., Palmer, K. J. and Ridout, M. S. (2007) Negative score test statistic. American statistician 61, 285--288.
AIC, LR.test
## Not run: ------------------------------------
# AIC (secrdemo.0, secrdemo.b)
# st <- score.test (secrdemo.0, g0 ~ b)
# st
# score.table(st)
#
# ## adding a time covariate to separate occasions (1,2) from (3,4,5)
# secrdemo.0$timecov <- data.frame(t2 = factor(c(1,1,2,2,2)))
# st2 <- score.test (secrdemo.0, g0 ~ t2)
# score.table(st,st2)
## ---------------------------------------------
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