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)
... |
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: |
ep |
A tolerance used in the algorithm: default |
acc |
A tolerance used in the algorithm: default |
See Evans and Forcina (2011).
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. |
Antonio Forcina, Giovanni M. Marchetti
Evans, R.J. and Forcina, A. (2013). Two algorithms for fitting constrained marginal models. Computational Statistics and Data Analysis, 66, 1-7.
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
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