MleCslogistic {cslogistic} | R Documentation |
Fit a conditional specified logistic regression model for multivariate binary responses.
MleCslogistic(formula,type = TRUE, intercept = TRUE, method = "BFGS", maxiter=1000 , data, ...)
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. |
Alejandro Jara Vallejos Alejandro.JaraVallejos@med.kuleuven.be
Maria Jose Garcia-Zattera MariaJose.GarciaZattera@med.kuleuven.be
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.
# 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)