Description Usage Arguments Value Author(s) References See Also Examples

This function generates a posterior density sample from a conditionally specified logistic regression model for multivariate binary data using a random walk Metropolis algorithm. The user supplies data and priors, and a sample from the posterior density is returned as a object, which can be subsequently analyzed with functions provided in the coda package.

1 2 3 |

`formula` |
Model formula. |

`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'. |

`burnin` |
The number of burn-in iterations for the sampler. |

`mcmc` |
The number of Metropolis iterations for the sampler. |

`thin` |
The thinning interval used in the simulation. The number of mcmc iterations must be divisible by this value. |

`tune` |
Metropolis tuning parameter. Make sure that the acceptance rate is satisfactory (typically between 0.20 and 0.5) before using the posterior density sample for inference. |

`beta.start` |
The starting value for the |

`b0` |
The prior mean of |

`B0` |
The prior precision of |

`...` |
further arguments to be passed. |

An mcmc object that contains the posterior density sample. This object can be summarized by functions provided by the coda package.

Alejandro Jara atjara@uc.cl

Maria Jose Garcia-Zattera mjgarcia@uc.cl

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ```
# 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 <- BayesCslogistic(y~age)
fit0
summary(fit0)
plot(fit0)
# different effects: only intercept
fit1 <- BayesCslogistic(y~age,type=FALSE)
fit1
summary(fit1)
plot(fit1)
# different effects: all the covariates
fit2 <- BayesCslogistic(y~age,type=FALSE,intercept=FALSE)
fit2
summary(fit2)
plot(fit2)
``` |

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