gIPFrm-package: Generalized Iterative Proportional Fitting for Relational...
In gIPFrm: Generalized Iterative Proportional Fitting for Relational Models

Description Details Note Author(s) References Examples

The package provides an iterative scaling procedure that computes the maximum likelihood estimates of the cell frequencies and of the model parameters under a relational model, with or without the overall effect.

Package:	gIPFrm
Type:	Package
Version:	3.1
Date:	2017-07-21
License:	GPL (>= 2)

The iterative proportional fitting procedure is called by the function g.ipf.

Tamas Rudas was supported in part by Grant K-106154 from the Hungarian National Scientific Research Fund (OTKA). The authors wish to thank Juraj Medzihorsky for his help with building this package.

Anna Klimova, Tamas Rudas

Maintainer: Anna Klimova <aklimova25@gmail.com>

A.Klimova, T.Rudas, A.Dobra, Relational models for contingency tables. J. Multivariate Anal., 2012, 104, 159–173.

A.Klimova, T.Rudas, Iterative proportional scaling for curved exponential families. Scand. J. Statist., 2015, 42, 832–847.

A. Klimova, Coordinate-Free Exponential Families on Contingency Tables. PhD thesis. Advisers: Tamas Rudas and Thomas Richardson.

A.Agresti, Categorical Data Analysis. Wiley, New York, 1990.

J.Aitchison, S.D.Silvey, Maximum-likelihood estimation procedures and associated tests of significance. J. Roy. Statist. Soc. Ser.B, 1960, 22, 154–171.

G.Kawamura, T.Matsuoka, T.Tajiri, M.Nishida, M.Hayashi, Effectiveness of a sugarcane-fish combination as bait in trapping swimming crabs. Fisheries Research, 1995, 22, 155–160.

### Multiplicative model from Aitchison and Silvey (1960)

A = matrix(c(1, 0, 0, 1, 0, 1, 1, 
             0, 1, 0, 1, 1, 0, 1,
             0, 0, 1, 0, 1, 1, 1), byrow=TRUE, nrow=3) ## the model matrix 

y = c(46,24,7,15,3,4,1) ## the observed data

g.ipf(A, y, 1e-4, "probabilities", "grid")
g.ipf(A, y, 1e-6, "probabilities", "bisection")