gIPFrm (version 3.1)

gIPFrm-package: Generalized Iterative Proportional Fitting for Relational Models

Description

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.

Details

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.

References

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.

Examples

# NOT RUN {
### 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")

# }