MleCslogistic {cslogistic}R Documentation

Perform a Maximum Likelihood Analysis of a conditionally specified logistic regression model

Description

Fit a conditional specified logistic regression model for multivariate binary responses.

Usage

MleCslogistic(formula,type = TRUE, intercept = TRUE, method = "BFGS",
           maxiter=1000 , data, ...)

Arguments

formula a symbolic description of the model to be fit.
type logical variable indicating if covariates have the same effect 'TRUE' or different effect 'FALSE' for each variable.
intercept logical variable indicating if only the intercept 'TRUE' or all the covariates have different effect 'FALSE' for each variable. The option 'type' must be 'FALSE'.
method the optimization method to be used; the default method is "BFGS".
maxiter maximum number of iterations used by the optimization method.
data an optional data frame containing the variables in the model. If not found in 'data', the variables are taken from 'environment(formula)', typically the environment from which 'cslogistic' is called..
... further arguments to be passed.

Author(s)

Alejandro Jara Vallejos Alejandro.JaraVallejos@med.kuleuven.be

Maria Jose Garcia-Zattera MariaJose.GarciaZattera@med.kuleuven.be

References

Garcia-Zattera, M. J., Jara, A., Lesaffre, E. and Declerck, D. (2005). On conditional independence for multivariate binary data in caries research. In preparation.

Joe, H. and Liu, Y. (1996). A model for multivariate response with covariates based on compatible conditionally specified logistic regressions. Satistics & Probability Letters 31: 113-120.

See Also

cslogistic, BayesCslogistic.

Examples

# simulated data set

library(mvtnorm)

n<-400
mu1<-c(-1.5,-0.5)
Sigma1<-matrix(c(1, -0.175,-0.175,1),ncol=2)
age<-as.vector(sample(seq(5,6,0.1),n,replace=TRUE))
beta1<-0.2

z<-rmvnorm(n,mu1,Sigma1)
zz<-cbind(z[,1]+beta1*age,z[,2]+beta1*age)
datos<-cbind(zz[,1]>0,zz[,2]>0,age)
colnames(datos)<-c("y1","y2","age")
data0<-data.frame(datos)
attach(data0)

# equal effect of age for all the covariates

y<-cbind(y1,y2)

fit0<-MleCslogistic(y~age)
fit0
summary(fit0)

# different effects: only intercept

fit1<-MleCslogistic(y~age,type=FALSE)
fit1
summary(fit1)

# different effects: all the covariates

fit2<-MleCslogistic(y~age,type=FALSE,intercept=FALSE)
fit2
summary(fit2)


[Package cslogistic version 0.1-1 Index]