Description Usage Arguments Value Author(s) References
Without using covariates (i.e., with capture probabilities assumed flat over the covariate space), select the best log-linear model for the marginal contingency table of capture pattern counts.
1 2 | flat.IC(pop, models = make.hierarchical.term.sets(k = attributes(dt)$k),
rasch = FALSE, ic = "AICc", adjust = FALSE, averaging = FALSE)
|
pop |
A data.frame containing 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. The default is |
rasch |
Logical: Should the Rasch model (most basic version, Darroch
et. al. 1993) be considered, in addition to standard models? |
ic |
Character string specifying the information criterion to use for model selection. Currently AIC, AICc, BIC, and BICpi are implemented. |
adjust |
Logical: Should we adjust the cells as in Evans and Bonett (1995)? |
averaging |
Logical: Should we use model averaging based on the information criterion scores? |
pred |
The point estimate of the population size |
form |
The log-linear terms in the chosen model |
Zach Kurtz
Fienberg SE (1972). "The Multiple Recapture Census for Closed Populations and Incomplete $2^k$ Contingency Tables." Biometrika, 59(3), pp. 591.
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