Description Usage Arguments Details Value Author(s) References
A high-level function to fit a VGAM CRC model to standardized data (the output of format.data).
1 2 3 | vgam.crc(dat, models = make.hierarchical.term.sets(k = attributes(dt)$k), sdf,
llform = NULL, round.vars = NULL, rounding.scale = NULL,
boot.control = NULL)
|
dat |
The CRC data, as output of |
models |
A list of models – or an expression that returns a
list of models – to be considered in local model search. Run the default,
|
sdf |
A vector, with length corresponding to the number of continuous predictor variables, that states the desired effective degrees of freedom for the corresponding smooth spline in VGAM. |
llform |
A character vector of predictors of the form "c1", "c2" for
main effects, or "c12" for an interaction. By default, the function
|
round.vars |
See |
rounding.scale |
See |
boot.control |
A list of control parameters for bootstrapping the sampling distribution of the estimator(s). By default, there is no bootstrapping. |
Implements, approximately, the method of Zwane (2004). Serves mainly as a user-friendly interface to the VGAM package.
est |
A point estimate of the population size |
llform |
The set of log-linear terms |
dat |
The output of function
|
aic |
The AICc for the chosen VGAM, as computed by
function |
mod |
The VGAM model object; see the
|
... |
The
output is of class |
Zach Kurtz
Zwane E and Heijden Pvd (2003). "Implementing the parametric bootstrap in capture-recapture models with continuous covariates." Statistics & Probability Letters, 65, pp. 121-125.
Zwane E and Heijden Pvd (2004). "Semiparametric models for capture-recapture studies with covariates." Computational Statistics & Data Analysis, 47, pp. 729-743.
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