derived(object, sessnum = NULL, groups = NULL, alpha = 0.05,
se.esa = FALSE, se.D = TRUE, loginterval = TRUE,
distribution = NULL, ncores = 1)
esa(object, sessnum = 1, beta = NULL, real = NULL, noccasions = NULL)secr object output from secr.fit, or an
object of class c("list","secrlist")object is an `secrlist'.distribution overrides the value
in object$details. The sampling variance of $\hat{D}$
from secr.fit by default is spatially unconditional
(distribution = "Poisson"). For sampling variance conditional on the population of the
habitat mask (and therefore dependent on the mask area), specify
distribution = "binomial". The equation for the conditional
variance includes a factor $(1 - a/A)$ that disappears in the
unconditional (Poisson) variance (Borchers and Efford 2007). Thus the
conditional variance is always less than the unconditional variance. The
unconditional variance may in turn be an overestimate or (more likely)
an underestimate if the true spatial variance is non-Poisson.
Derived parameters may be estimated for population subclasses (groups)
defined by the user with the groups argument. Each named factor
in groups should appear in the covariates dataframe of
object$capthist (or each of its components, in the case of a
multi-session dataset).
esa is used by derived to compute individual-specific
effective sampling areas: $$a_i (\hat{\theta}) = \int _A \:
p.(\mathbf{X};\mathbf{z}_i, \mathbf{\hat{\theta}}) \; \mathrm{d}
\mathbf{X}$$ where
$p.(\mathbf{X})$ is the probability an individual at X is
detected at least once and the $\mathbf{z}_i$ are optional
individual covariates. Integration is over the area $A$ of the
habitat mask.
The argument noccasions may be used to vary the number of
sampling occasions; it works only when detection parameters are constant
across individuals and across time.
The effective sampling area `esa' ($a(\hat{\theta})$)
reported by derived is equal to the harmonic mean of the
$a_i (\hat{\theta})$ (arithmetic
mean prior to version 1.5). The sampling variance of
$a(\hat{\theta})$ is estimated by
$$\widehat{\mbox{var}}(a(\hat{\theta})) = \hat{G}_\theta^T
\hat{V}_\theta \hat{G}_\theta,$$
where $\hat{V}$ is the asymptotic estimate of the
variance-covariance matrix of the beta detection parameters
($\theta$) and $\hat{G}$ is a numerical estimate
of the gradient of $a(\theta)$ with respect to
$\theta$, evaluated at $\hat{\theta}$.
A 100(1--alpha)% asymptotic confidence interval is reported for
density. By default, this is asymmetric about the estimate because the
variance is computed by backtransforming from the log scale. You may
also choose a symmetric interval (variance obtained on natural scale).
The vector of detection parameters for esa may be specified via
beta or real, with the former taking precedence. If
neither is provided then the fitted values in object$fit$par are
used. Specifying real parameter values bypasses the various
linear predictors. Strictly, the `real' parameters are for a naive
capture (animal not detected previously).
The computation of sampling variances is relatively slow and may be
suppressed with se.esa and se.D as desired.
If ncores > 1 the predict.secr, print.secr,
secr.fit, empirical.varD## extract derived parameters from a model fitted previously
## by maximizing the conditional likelihood
derived (secrdemo.CL)
## what happens when sampling variance is conditional on mask N?
derived(secrdemo.CL, distribution = "binomial")
## fitted g0, sigma
esa(secrdemo.CL)
## force different g0, sigma
esa(secrdemo.CL, real = c(0.2, 25))Run the code above in your browser using DataLab