loglinb2 {VGAM} | R Documentation

## Loglinear Model for Two Binary Responses

### Description

Fits a loglinear model to two binary responses.

### Usage

    loglinb2(exchangeable = FALSE, zero = NULL)

### Arguments

`exchangeable` | Logical. If `TRUE`, the two marginal probabilities are constrained to be equal. Should be set `TRUE` for ears, eyes, etc. data.
`zero` | Which linear/additive predictor is modelled as an intercept only? A `NULL` means none of them.

### Details

The model is

P(Y1=y1,Y2=y2) = exp(u0 + u1*y1 + u2*y2 + u12*y1*y2)

where y1 and y2 are 0 or 1, and the parameters are u1, u2, u12. The normalizing parameter u0 can be expressed as a function of the other parameters, viz.,

u0 = -log[1 + exp(u1) + exp(u2) + exp(u1 + u2 + u12)].

The linear/additive predictors are (eta1,eta2,eta3) = (u1,u2,u12).

### Value

An object of class `"vglmff"` (see `vglmff-class`). The object is used by modelling functions such as `vglm`, `rrvglm` and `vgam`.
When fitted, the `fitted.values` slot of the object contains the four joint probabilities, labelled as (Y1,Y2) = (0,0), (0,1), (1,0), (1,1), respectively.

### Note

The response must be a two-column matrix of ones and zeros only. This is more restrictive than `binom2.or`, which can handle more types of input formats. Note that each of the 4 combinations of the multivariate response need to appear in the data set.

### Author(s)

Thomas W. Yee

### References

Yee, T. W. and Wild, C. J. (2001) Discussion to: ``Smoothing spline ANOVA for multivariate Bernoulli observations, with application to ophthalmology data (with discussion)'' by Gao, F., Wahba, G., Klein, R., Klein, B. Journal of the American Statistical Association, 96, 127–160.

McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, 2nd ed. London: Chapman & Hall.

Documentation accompanying the VGAM package at http://www.stat.auckland.ac.nz/~yee contains further information and examples.

### See Also

`binom2.or`, `binom2.rho`, `loglinb3`.

### Examples

    coalminers = transform(coalminers, age=(age-42)/5)

    # Get the n x 4 matrix of counts
    temp = vglm(cbind(nBnW,nBW,BnW,BW) ~ age, binom2.or, coalminers)
    counts = round(c(weights(temp, type="prior")) * temp@y)

    # Create a n x 2 matrix response for loglinb2()
    # bwmat = matrix(c(0,0, 0,1, 1,0, 1,1), 4, 2, byrow=TRUE)
    bwmat = cbind(bln=c(0,0,1,1), wheeze=c(0,1,0,1))
    matof1 = matrix(1, nrow(counts), 1)
    newminers = data.frame(bln = kronecker(matof1, bwmat[,1]),
                           wheeze = kronecker(matof1, bwmat[,2]),
                           wt = c(t(counts)),
                           age = with(coalminers, rep(age, rep(4, length(age)))))
    newminers = newminers[with(newminers, wt) > 0,]

    fit = vglm(cbind(bln,wheeze) ~ age, loglinb2, wei=wt, data=newminers)
    coef(fit, mat=TRUE)    # Same! (at least for the log odds-ratio)
    summary(fit)

    # Try reconcile this with McCullagh and Nelder (1989), p.234
    (0.166-0.131) / 0.027458   # 1.275 is approximately 1.25

[Package VGAM version 0.7-9 Index]