ggm (version 2.5)

fitmlogit: 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)

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.

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.

Author

Antonio Forcina, Giovanni M. Marchetti

Details

See Evans and Forcina (2011).

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