score.test {secr} | R Documentation

## Score Test for SECR Models

### Description

Compute score tests comparing a fitted model and a more general alternative model.

### Usage

    score.test(secr, ..., betaindex = NULL, trace = FALSE, ncores = 1, .relStep = 0.001,
        minAbsPar = 0.1)

    score.table(object, ..., sort = TRUE, dmax = 10)

### Arguments

`secr` | fitted secr model
`...` | one or more alternative models OR a fitted secr model
`trace` | logical. If TRUE then output one-line summary at each evaluation of the likelihood
`ncores` | integer number of cores available for parallel processing
`.relStep` | see `fdHess`
`minAbsPar` | see `fdHess`
`betaindex` | vector of indices mapping fitted values to parameters in the alternative model
`object` | score.test object or list of such objects
`sort` | logical for whether output rows should be in descending order of AICc
`dmax` | threshold of dAICc for inclusion in model set

### Details

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~1, g0~1, sigma~1) to one with a behavioural effect on g0 (D~1, g0~b, sigma~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.

### Value

An object of class ‘score.test’ that inherits from ‘htest’, a list with components

`statistic` | the value the chi-squared test statistic (score statistic)
`parameter` | degrees of freedom of the approximate chi-squared distribution of the test statistic (difference in number of parameters H0, H1)
`p.value` | probability of test statistic assuming chi-square distribution
`method` | a character string indicating the type of test performed
`data.name` | character string with null hypothesis, alternative hypothesis and arguments to function call from fit of H0
`H0` | simpler model
`np0` | number of parameters in simpler model
`H1` | alternative model
`H1.beta` | coefficients of alternative model
`AIC` | Akaike's information criterion, approximated from score statistic
`AICc` | AIC with small-sample adjustment of Hurvich & Tsai 1989

If ... 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.

### Note

This implementation is experimental. The AIC values, and values derived from them, are approximations that may differ considerably from AIC values obtained by fitting and comparing the respective models. Use of the observed information matrix may not be optimal.

### References

Catchpole, E. A. and Morgan, B. J. T. (1996) Model selection of ring-recovery models using score tests. Biometrics 52, 664–672.

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.

### See Also

`AIC`, `LR.test`

### Examples

    ## 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)

    ## End(Not run)

[Package secr version 3.2.1 Index]