These functions compute a goodness-of-fit test for N-mixture models based on Pearson's chi-square.
##methods for 'unmarkedFitPCount', 'unmarkedFitPCO',
##'unmarkedFitDS', 'unmarkedFitGDS', 'unmarkedFitGMM',
##'unmarkedFitGPC', and 'unmarkedFitMPois' classes
Nmix.chisq(mod, …)Nmix.gof.test(mod, nsim = 5, plot.hist = TRUE, report = NULL, …)
the N-mixture model of 'unmarkedFitPCount', 'unmarkedFitPCO', 'unmarkedFitDS', 'unmarkedFitGDS', 'unmarkedFitGMM', 'unmarkedFitGPC', or 'unmarkedFitMPois' classes for which a goodness-of-fit test is required.
the number of bootstrapped samples.
logical. Specifies that a histogram of the bootstrapped test statistic is to be included in the output.
If NULL, the test statistic for each iteration is
not printed in the terminal. Otherwise, an integer indicating the
number of values of the test statistic that should be printed on
the same line. For example, if report = 3, the values of the
test statistic for three iterations are reported on each line.
additional arguments passed to the function.
Nmix.chisq returns two value:
the Pearson chi-square statistic.
the class of the fitted model.
Nmix.gof.test returns the following components:
the class of the fitted model.
the Pearson chi-square statistic.
the bootstrapped chi-square test statistics (i.e., obtained for each of the simulated data sets).
the P-value assessed from the parametric bootstrap, computed as the proportion of the simulated test statistics greater than or equal to the observed test statistic.
the estimate of the overdispersion parameter, c-hat, computed as the observed test statistic divided by the mean of the simulated test statistics.
the number of bootstrap samples. The recommended number of samples varies with the data set, but should be on the order of 1000 or 5000, and in cases with a large number of visits, even 10 000 samples, namely to reduce the effect of unusually small values of the test statistics.
The Pearson chi-square can be used to assess the fit of N-mixture
models. Instead of relying on the theoretical distribution of the
chi-square, a parametric bootstrap approach is implemented to obtain
P-values with the parboot function of the unmarked
package. Nmix.chisq computes the observed chi-square statistic
based on the observed and expected counts from the model.
Nmix.gof.test calls internally Nmix.chisq and
parboot to generate simulated data sets based on the model and
compute the chi-square test statistic.
It is also possible to obtain an estimate of the overdispersion parameter (c-hat) for the model at hand by dividing the observed chi-square statistic by the mean of the statistics obtained from simulation (MacKenzie and Bailey 2004, McKenny et al. 2006). This method of estimating c-hat is similar to the one implemented for capture-mark-recapture models in program MARK (White and Burnham 1999).
Note that values of c-hat > 1 indicate overdispersion (variance > mean). Values much higher than 1 (i.e., > 4) probably indicate lack-of-fit. In cases of moderate overdispersion, one can multiply the variance-covariance matrix of the estimates by c-hat. As a result, the SE's of the estimates are inflated (c-hat is also known as a variance inflation factor).
In model selection, c-hat should be estimated from the global model and the same value of c-hat applied to the entire model set. Specifically, a global model is the most complex model which can be simplified to yield all the other (nested) models of the set. When no single global model exists in the set of models considered, such as when sample size does not allow a complex model, one can estimate c-hat from 'subglobal' models. Here, 'subglobal' models denote models from which only a subset of the models of the candidate set can be derived. In such cases, one can use the smallest value of c-hat for model selection (Burnham and Anderson 2002).
Note that c-hat counts as an additional parameter estimated and should
be added to K. All functions in package AICcmodavg
automatically add 1 when the c.hat argument > 1 and apply the
same value of c-hat for the entire model set. When c-hat > 1, functions
compute quasi-likelihood information criteria (either QAICc or QAIC,
depending on the value of the second.ord argument) by scaling the
log-likelihood of the model by c-hat. The value of c-hat can influence
the ranking of the models: as c-hat increases, QAIC or QAICc will favor
models with fewer parameters. As an additional check against this
potential problem, one can generate several model selection tables by
incrementing values of c-hat to assess the model selection uncertainty.
If ranking changes only slightly up to the c-hat value observed, one can
be confident in making inference.
In cases of underdispersion (c-hat < 1), it is recommended to keep the value of c-hat to 1. However, note that values of c-hat << 1 can also indicate lack-of-fit and that an alternative model should be investigated.
Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach. Second edition. Springer: New York.
MacKenzie, D. I., Bailey, L. L. (2004) Assessing the fit of site-occupancy models. Journal of Agricultural, Biological, and Environmental Statistics 9, 300--318.
McKenny, H. C., Keeton, W. S., Donovan, T. M. (2006). Effects of structural complexity enhancement on eastern red-backed salamander (Plethodon cinereus) populations in northern hardwood forests. Forest Ecology and Management 230, 186--196.
White, G. C., Burnham, K. P. (1999). Program MARK: Survival estimation from populations of marked animals. Bird Study 46 (Supplement), 120--138.
AICc, c_hat, evidence,
modavg, importance,
mb.gof.test, modavgPred,
pcount, pcountOpen,
parboot
# NOT RUN {
##N-mixture model example modified from ?pcount
# }
# NOT RUN {
require(unmarked)
##single season
data(mallard)
mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs = mallard.site,
obsCovs = mallard.obs)
##run model
fm.mallard <- pcount(~ ivel+ date + I(date^2) ~ length + elev + forest,
mallardUMF, K=30)
##compute observed chi-square
obs <- Nmix.chisq(fm.mallard)
obs
##round to 4 digits after decimal point
print(obs, digits.vals = 4)
##compute observed chi-square, assess significance, and estimate c-hat
obs.boot <- Nmix.gof.test(fm.mallard, nsim = 10)
##note that more bootstrap samples are recommended
##(e.g., 1000, 5000, or 10 000)
obs.boot
print(obs.boot, digits.vals = 4, digits.chisq = 4)
detach(package:unmarked)
# }
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