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.
1 2 3 4 5 6 7 8 9 10 | ### 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")
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