Select and fit an LLM

Description

Use an information criterion to select a local log-linear model

Usage

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ic.fit(densi, models, N, ic, averaging = averaging, normalized = TRUE,
  rasch = FALSE)

Arguments

densi

A matrix with one row and 2^k-1 column containing cell counts or empirical cell probabilities corresponding to all the possible capture patterns.

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().

N

If you multiply densi by N and then sum over the resulting vector, you should get the effective sample size.

ic

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

averaging

Logical: TRUE means that we use information criterion scores to do model averaging.

normalized

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

rasch

Logical: TRUE means that the Rasch model is a candidate.

Details

Just like flat.IC except that it is designed to take in a local average instead of a full capture-recapture dataset

Value

pred

Estimated rate of missingness for the selected model

form

Formula of the selected model

Author(s)

Zach Kurtz

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