Build a VGAM CRC model
Description
A highlevel function to fit a VGAM CRC model to standardized data (the output of format.data).
Usage
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)

Arguments
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. 
Details
Implements, approximately, the method of Zwane (2004). Serves mainly as a userfriendly interface to the VGAM package.
Value
est 
A point estimate of the population size 
llform 
The set of loglinear 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 
Author(s)
Zach Kurtz
References
Zwane E and Heijden Pvd (2003). "Implementing the parametric bootstrap in capturerecapture models with continuous covariates." Statistics & Probability Letters, 65, pp. 121125.
Zwane E and Heijden Pvd (2004). "Semiparametric models for capturerecapture studies with covariates." Computational Statistics & Data Analysis, 47, pp. 729743.