A relative density model (one fitted by maximising the conditional likelihood) has a zero intercept for density on the default log scale (the intercept is 1.0 for the identity link).
The constant k that relates absolute density to relative density \(D^\prime = k^{-1} D\) is obtained as described in Efford (2025).
These functions infer the value of k and use this to construct the predicted density surface
(predictDsurface is the equivalent function for full-likelihood models).
The derived method for `secr' objects performs the same calculation as derivedDcoef
and also back-transforms the intercept and computes a delta-method estimate of its variance and confidence limits.
However that variance estimate is unreliable.
derivedDfit constructs an secr fitted model object resembling that from an unconditional model fit.
# S3 method for secr
derivedDcoef(object, se = FALSE, ...)
# S3 method for secrlist
derivedDcoef(object, se = FALSE, ...)derivedDsurface(object, mask = NULL, sessnum = NULL, groups = NULL)
derivedDfit(object, vcv = TRUE)
For derivedDcoef --
A dataframe mimicking coef(object) with an initial row for the derived density intercept \(\beta_0\).
For an identity link, subsequent density coefficients are scaled by the derived \(\beta_0\).
For derivedDsurface --
Dsurface object with class c("Dsurface", "mask", "data.frame"). Multi-session if object is multi-session and sessnum = NULL.
If groups are defined the result is a list of Dsurfaces.
fitted secr relative density model
logical; if TRUE the variance of beta0 is estimated by the delta method
new mask for which to compute densities
integer session number
character vector of covariate names to define groups (optional)
logical; if TRUE output includes the extended variance-covariance matrix
other arguments (not used)
The theory is provided by Efford in prep.
derivedDcoef(object, se = TRUE) is equivalent to derived(object, Dweight = TRUE),
although derivedDcoef returns estimates on the link scale.
Efford, M. G. In prep. SECR models for relative density.
derived
predictDsurface