These methods return predicted values from fitted model objects.
signature(object = "unmarkedFit")"type" must be either `state' or `det'.
signature(object = "unmarkedFitColExt")"type" must be 'psi', 'col', 'ext', or 'det'.
signature(object = "unmarkedFitGMM")"type" must be 'lambda', 'psi', 'det'
signature(object = "unmarkedFitList")"type" depends upon the fitted models
signature(object = "unmarkedRanef")Use this method to generate the empirical Bayes posterior predictive distribution for functions of the random variables (latent abundance or occurrence).
In addition to the output object from ranef, you must also supply a
custom function to argument func. The function must take as input a matrix
with dimensions M x T, where M is the number of sites and T is the number of
primary periods (T=1 for single-season models). The output of this function should
be a vector or matrix containing the derived parameters of interest.
You may also manually set the number of draws from the posterior predictive
distribution with argument nsims; the default is 100.
The output of predict will be a vector or array with one more dimension
than the output of the function supplied func, corresponding to the number
of draws requested nsims. For example, if func
outputs a scalar, the output of predict will be a vector with length
equal to nsims. If func outputs a 3x2 matrix, the output of
predict will be an array with dimensions 3x2xnsims.
See ranef for an example.
Alternatively, you can use the posteriorSamples function on the
ranef output object to obtain the full posterior predictive distribution.
This is useful if you are having trouble designing your custom function or if
you want to obtain multiple different derived parameters from the same posterior
predictive distribution.