fitmlogit: Multivariate logistic models

In ggm: Graphical Markov Models with Mixed Graphs

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

ggm documentation built on March 26, 2020, 7:49 p.m.