Compute an IC for several LLMs

Description

Given several sets of log-linear terms, compute the IC corresponding to each model.

Usage

1
ic.all(models, ddat, ic, normalized = normalized)

Arguments

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 make.hierarchical.term.sets().

ddat

The log-linear design matrix.

ic

The information criterion, such as AIC, AICc, BIC, or BICpi.

normalized

Logical: TRUE means that beta0 will be adjusted so that the log-linear model corresponds to cell probabilities instead of expected cell counts.

Value

A matrix with as many rows as there are entries in models. The columns contain the point estimates of the population size, the information criterion scores, and the information criterion weights for all the models, which sum to one

Author(s)

Zach Kurtz

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