cslogistic (version 0.1-2)

MleCslogistic: 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.

References

Garcia-Zattera, M. J., Jara, A., Lesaffre, E. and Declerck, D. (2007). Conditional independence of multivariate binary data with an application in caries research. Computational Statistics and Data Analysis, 51(6): 3223-3232.

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)
  agev <- as.vector(sample(seq(5,6,0.1),n,replace=TRUE))
  beta1 <- 0.2

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