ggm (version 2.3)

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)

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.

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.

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

# NOT RUN {
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
# }