R: Generalized Log-Linear Fitting

mygllm {RecordLinkage} | R Documentation

## Generalized Log-Linear Fitting

### Description

Fits a log-linear model for collapsed contingency tables.

### Usage

    mygllm(y, s, X, maxit = 1000, tol = 1e-05, E = rep(1, length(s)))

### Arguments

`y` | Vector of observed cell frequencies.
`s` | Scatter matrix. s[i] is the cell in the observed array that corresponds to cell i in the full array.
`X` | Design matrix.
`maxit` | Maximum number of iterations.
`tol` | Convergence parameter.
`E` | Full contingency table. Should be initialized with either ones or a priori estimates.

### Details

This is an implementation and extension of the algorithm published by Haber (1984). It also incorporates ideas of David Duffy (see references).

A priori estimates of the full contingency table can be given as start values by argument `E`. This can reduce execution time significantly.

### Value

Estimated full contingency table.

### Author(s)

Andreas Borg, Murat Sariyar

### References

Michael Haber, Algorithm AS 207: Fitting a General Log-Linear Model, in: Applied Statistics 33 (1984) No. 3, 358–362.

David Duffy: gllm: Generalised log-linear model. R package version 0.31. https://cran.r-project.org/package=gllm

### See Also

`emWeights`, which makes use of log-linear fitting for weight calculation.

[Package RecordLinkage version 0.4-12.2 Index]