R: Multivariate logistic models

fitmlogit {ggm} | R Documentation

## Multivariate logistic models

### Description

Fits a logistic regression model to multivariate binary responses.

### Usage

    fitmlogit(..., C = c(), D = c(), data, mit = 100, ep = 1e-80, acc = 1e-04)

### Arguments

`...` | Model formulae of marginal logistic models for each response and for each association parameters (log-odds ratios).
`C` | Matrix of equality constraints.
`D` | Matrix of inequality cosntraints.
`data` | A data frame containing the responses and the explanatory variables.
`mit` | A positive integer: maximum number of iterations. Default: `100`.
`ep` | A tolerance used in the algorithm: default `1e-80`.
`acc` | A tolerance used in the algorithm: default `1e-4`.

### Details

See Evans and Forcina (2011).

### Value

`LL` | The maximized log-likelihood.
`be` | The vector of the Maximum likelihood estimates of the parameters.
`S` | The estimated asymptotic covariance matrix.
`P` | The estimated cell probabilities for each individual.

### Author(s)

Antonio Forcina, Giovanni M. Marchetti

### References

Evans, R.J. and Forcina, A. (2013). Two algorithms for fitting constrained marginal models. Computational Statistics and Data Analysis, 66, 1-7.

### See Also

`glm`

### Examples

    data(surdata)
    out1 <- fitmlogit(A ~X, B ~ Z, cbind(A, B) ~ X*Z, data = surdata)
    out1$beta
    out2 <- fitmlogit(A ~X, B ~ Z, cbind(A, B) ~ 1, data = surdata)
    out2$beta

[Package ggm version 2.3 Index]