### Name: maop ### Title: Mixed Autoregressive Ordered Probit Models ### Aliases: maop print.maop summary.maop ### Keywords: Longitudinal data, ordinal categorical response, pairwise ### likelihood, random effects. ### ** Examples # Fits a MAOP model to migraine data (Varin and Czado, 2009) data(migraine) attach(migraine) # covariates university <-rep(0,length(HA)) university[ED==5]<-1 university[ED==6]<-1 university[ED==7]<-1 university <- as.factor(university==1) analgesics <- as.factor(MEDANALG=="Y") # weather covariates wc <- factor(cut(MINWC, breaks=c(-50, -10, 0, 10, 30))) high.pressure <- PMN>10130 high.pressure.yesterday <- PMN1P>10130 change <- rep("persistence", length(high.pressure)) change[(!high.pressure & high.pressure.yesterday)] <- "worse" change[(high.pressure & !high.pressure.yesterday)] <- "better" change <- factor(change) humidity <- cut(RHMN, breaks=c(0, 60, 80, 100)) # model m <- HA~university+analgesics+wc+humidity+change # fit model m with different autocorrelation parameters # for patients with analgesics intake and those who not # try with pairwise likelihood with maximal distance 2 maop2 <- maop(m, subjects=SUBJECT, periods=PERIOD, randomEffect=TRUE, dynamics=analgesics, distance=2) summary(maop2, digits=2) # pairwise likelihood with maximal distance equals to 6 maop6 <- maop(m, subjects=SUBJECT, periods=PERIOD, randomEffect=TRUE, dynamics=analgesics, distance=6, startValues=maop2$coeff) summary(maop6, digits=2)