fitmlogit function

Multivariate logistic models

Fits a logistic regression model to multivariate binary responses.

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).

Returns

• 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.

References

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

Author(s)

Antonio Forcina, Giovanni M. Marchetti

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