Can multinomial models be estimated using Generalized Linear model?

Question

In analysis of categorical data, we often use logistic regression to estimate relationships between binomial outcomes and one or more covariates.

I understand this is a type of generalized linear model (GLM). In R, this is implemented with the glm function using the argument family=binomial. On the other hand, in categorical data analysis are multinomial models. Are these not GLMs? And can't they be estimated in R using the glm function?

(In this post for Multinomial Logistic Regression. The author uses an external package mlogit, which seems also outdated)

Why is the class of GLMs restricted to dichotomous outcomes? Is it because multi-class classification can be treated as multiple binary classification models?

Answer 1

The GLMs in R are estimated with Fisher Scoring. Two approaches to multi-category logit come to mind: proportional odds models and log-linear models or multinomial regression.

The proportional odds model is a special type of cumulative link model and is implemented in the MASS package. It is not estimated with Fisher scoring, so the default glm.fit work-horse would not be able to estimate such a model. Interestingly, however, cumulative link models are GLMs and were discussed in the eponymous text by McCullogh and Nelder. A similar issue is found with negative binomial GLMs: they are GLMs in the strict sense of a link function, and a probability model, but require specialized estimation routines. As far as the R function glm, one should not look at it as an exhaustive estimator for every type of GLM.

nnet has an implementation of a loglinear model estimator. It is conformed to their more sophisticated neural net estimator using soft-max entropy, which is an equivalent formulation (theory is there to show this). It turns out you can estimate log-linear models with glm in default R if you're keen. The key lies in seeing the link between logistic and poisson regression. Recognizing the interaction terms of a count model (difference in log relative rates) as a first order term in a logistic model for an outcome (log odds ratio), you can estimate the same parameters and the same SEs by "conditioning" on the margins of the $K \times 2$ contingency table for a multi-category outcome. A related SE question on that background is here

Take as an example the following using the VA lung cancer data from the MASS package:

> summary(multinom(cell ~ factor(treat), data=VA)) # weights:  12 (6 variable) initial  value 189.922327  iter  10 value 182.240520 final  value 182.240516  converged Call: multinom(formula = cell ~ factor(treat), data = VA)  Coefficients:     (Intercept) factor(treat)2 2  6.931413e-01     -0.7985009 3 -5.108233e-01      0.4054654 4 -9.538147e-06     -0.5108138  Std. Errors:   (Intercept) factor(treat)2 2   0.3162274      0.4533822 3   0.4216358      0.5322897 4   0.3651485      0.5163978  Residual Deviance: 364.481  AIC: 376.481  

Compared to:

> VA.tab <- table(VA[, c('cell', 'treat')]) > summary(glm(Freq ~ cell * treat, data=VA.tab, family=poisson))  Call: glm(formula = Freq ~ cell * treat, family = poisson, data = VA.tab)  Deviance Residuals:  [1]  0  0  0  0  0  0  0  0  Coefficients:                Estimate Std. Error z value Pr(>|z|)     (Intercept)   2.708e+00  2.582e-01  10.488   <2e-16 *** cell2         6.931e-01  3.162e-01   2.192   0.0284 *   cell3        -5.108e-01  4.216e-01  -1.212   0.2257     cell4        -1.571e-15  3.651e-01   0.000   1.0000     treat2        2.877e-01  3.416e-01   0.842   0.3996     cell2:treat2 -7.985e-01  4.534e-01  -1.761   0.0782 .   cell3:treat2  4.055e-01  5.323e-01   0.762   0.4462     cell4:treat2 -5.108e-01  5.164e-01  -0.989   0.3226     --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  (Dispersion parameter for poisson family taken to be 1)      Null deviance: 1.5371e+01  on 7  degrees of freedom Residual deviance: 4.4409e-15  on 0  degrees of freedom AIC: 53.066  Number of Fisher Scoring iterations: 3 

Compare the interaction parameters and the main levels for treat in the one model to the second. Compare also the intercept. The AICs are different because the loglinear model is a probability model for even the margins of the table which are conditioned upon by other parameters in the model, but in terms of prediction and inference these two approaches yield identical results.

So in short, trick question! glm handles multi-category logistic regression, it just takes a greater understanding of what constitutes such models.