Compute profile likelihood confidence intervals for `beta' or `real' parameters of a spatially explicit capture-recapture model,
# S3 method for secr
confint (object, parm, level = 0.95, newdata = NULL,
tracelevel = 1, tol = 0.0001, bounds = NULL, ...)secr model object A matrix with one row for each parameter in parm, and columns
giving the lower (lcl) and upper (ucl) 100*level
If parm is numeric its elements are interpreted as the indices of
`beta' parameters; character values are interpreted as `real'
parameters. Different methods are used for beta parameters and real
parameters. Limits for the \(j\)-th beta parameter are found by a
numerical search for the value satisfying \(-2(l_j(\beta_j) - l) =
q\), where \(l\) is the maximized log
likelihood, \(l_j(\beta_j)\) is the maximized profile log
likelihood with \(\beta_j\) fixed, and \(q\) is the
\(100(1-\alpha)\) quantile of the
\(\chi^2\) distribution with one degree of freedom. Limits
for real parameters use the method of Lagrange multipliers (Fletcher and
Faddy 2007), except that limits for constant real parameters are
backtransformed from the limits for the relevant beta parameter.
If bounds is provided it should be a 2-vector or matrix of 2
columns and length(parm) rows.
Evans, M. A., Kim, H.-M. and O'Brien, T. E. (1996) An application of profile-likelihood based confidence interval to capture--recapture estimators. Journal of Agricultural, Biological and Experimental Statistics 1, 131--140.
Fletcher, D. and Faddy, M. (2007) Confidence intervals for expected abundance of rare species. Journal of Agricultural, Biological and Experimental Statistics 12, 315--324.
Venzon, D. J. and Moolgavkar, S. H. (1988) A method for computing profile-likelihood-based confidence intervals. Applied Statistics 37, 87--94.
## Not run: ------------------------------------
# ## Limits for the constant real parameter "D"
# confint(secrdemo.0, "D")
## ---------------------------------------------
Run the code above in your browser using DataLab