Description Usage Arguments Details Value Author(s) References
Select LLLMs for each row of the input data.
1 | apply.ic.fit(ydens, models, ess, mct, ic, cell.adj, averaging, loud = TRUE)
|
ydens |
A matrix with 2^k-1 columns, one for each capture pattern. Each row sums to 1; these are empirical capture pattern probabilities. |
models |
A list of character vectors, with each vector containing
column names from the associated log-linear design matrix.
For example, see the output of |
ess |
A vector of effective sample sizes, one for each row of ydens. |
mct |
The number of population units that were observed for each row of ydens. |
ic |
The chosen information criterion. Currently implemented: "AIC", "AICc", "BIC", "BICpi". |
cell.adj |
Logical: TRUE means that the cell adjustment of Evans and Bonet (1995) is applied. |
averaging |
Logical: TRUE means that the information criterion weights are used to do model averaging, locally. |
loud |
Logical: TRUE means that the progress is noted by printing the number of the row of ydens currently being processed. |
See Kurtz (2013). Each row of ydens
corresponds to a covariate
vector, and contains a local average of multinomial capture pattern outcomes
across nearby points. apply.ic.fit
applies the function
ic.fit
at each row. The vector of local effective sample sizes is
crucial, and is specified in the ess
argument.
lll |
An object of class "lllcrc" |
Zach Kurtz
Kurtz (2013)
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