Description Usage Arguments Author(s) References See Also Examples
Fit a conditional specified logistic regression model for multivariate binary responses.
1 2 |
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 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 | # 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)
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