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statusagga
2006 Feb 01
1
glm-logistic on discrete-time methods with individual and aggregated data
...3), 1, rep(1:2,2), rep(1:3 , 2), rep(1:4,
2))
statusIND <- c(rep(0 ,12), 1, rep(0:1,2), rep(c(0,0,1), 2),
rep(c(0,0,0,1),2))
datiIND <- as.data.frame(cbind(timesIND, statusIND))
datiIND$timesIND <- as.factor(datiIND$timesIND)
timesAGG <- c( 1:4, 1, 1:2, 1:3, 1:4)
statusAGG <- c(rep(0,4), 1, 0:1, c(0,0,1), c(0,0,0,1))
weightAGG <- c(rep(3,4), 1, rep(2,2), rep(2,3), rep(2,4))
datiAGG <- as.data.frame(cbind(timesAGG, statusAGG, weightAGG))
datiAGG$timesAGG <- as.factor(datiAGG$timesAGG)
coef(glm(statusIND ~ timesIND, family=binomial, data=datiIND))...