Rd file 'MVfill.Rd':
  \examples lines wider than 100 characters:
     imputedData <- MVfill( fm.est2.NA, states=dataEG2.NA[,2], chain.id=dataEG2.NA[,1], X=dataEG2.NA[,3:4])

Rd file 'RMC.mod.Rd':
  \usage lines wider than 90 characters:
     RMC.mod( states, chain.id=NULL, X=NULL, phi.id=NULL, pi.id=NULL, vcov=FALSE, inits=NULL, contr=list( maxit=1000, epsg=1e-8, epsf=1e-8,  ... [TRUNCATED]

Rd file 'RMC.pred.Rd':
  \examples lines wider than 100 characters:
     fm.est1 <- RMC.mod( states=dataEG2[,2], chain.id=dataEG2[,1], X=dataEG2[,3:4], vcov=TRUE)       #estimate the model
     n.cats1 <- fm.est1$stuff$n.cats; n.cats2 <- 5; n.covars <- 2; n.covars2 <- n.covars*n.cats1 + n.covars
     #specify parameter values, note that parameterisation gives state 1 as a reference state for all categories. This is for convenience on ... [TRUNCATED]
     gamma <- matrix( rnorm( n.covars2*n.cats2, sd=0.5), nrow=n.covars2, ncol=n.cats2)       #initial design matrix
     chains2 <- sim.chain( n.chains=5, n.obs=rep( 1000, 5), n.cats=n.cats2, n.covars=n.covars2, beta=beta, gamma=gamma, X=X2)        #simula ... [TRUNCATED]
     my.pi.id <- apply( beta, FUN=function(x){if( any( x!=0)) 1 else 0}, MARG=1)     #model controlling matrix
     fm.est2 <- RMC.mod( states=chains2[,2], chain.id=chains2[,1], X=X2, phi.id=my.phi.id, pi.id=my.pi.id, vcov=TRUE)        #estimate the m ... [TRUNCATED]
     tmp <- cbind( table( chains2[,"state"]) / nrow( chains2), pred2cond$area, sqrt( diag( pred2cond$vcov)), pred2marg$area, sqrt( diag( pre ... [TRUNCATED]
     colnames( tmp) <- c("observed","conditional prediction", "conditional se", "marginal prediction", "marginal se")

Rd file 'diagnos.Rd':
  \usage lines wider than 90 characters:
     diagnos.envel( obs.states, chain.id, X=NULL, fit, perc=c( 0.025, 0.975), B=100, contr=list( print.iter=50))

Rd file 'examplesForDiagnostics.Rd':
  \examples lines wider than 100 characters:
     B<-50   #number of simulations for simulated envelopes. The paper uses 1000 but this can be pretty slow
     chain <- sim.chain( n.chains=nc, n.obs=rep( n, nc), n.cats=n.cats, n.covars=1, beta=c(0,0.3,-0.3,0), gamma=c(0.5,0.2,1,0))
     plot( 1:m, head( chain[chain[,"chain"]==2,"state"],m), type='b', pch=19, main="Start of Example Chain", ylab="State", xlab="Index", axe ... [TRUNCATED]
     temp.est <- diagnos.envel( obs.states=chain[,2], chain.id=chain[,1], X=chain[,3,drop=FALSE], fit=fm.est, B=B)
     hrplot( temp.est[["patch"]][[my.cat]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Example Data, state 2" ... [TRUNCATED]
     hrplot( temp.est[["patch"]][[my.cat]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Example Data, state 3" ... [TRUNCATED]
     hrplot( temp.est$movement, ylab="Movement Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Example Data", pch=20)
     gpar <- matrix( c( c( -0.6, 0), c( 1.1, 1.5), c( 0.2, 0), c( -1.25, -1.5)), nrow=n.covs, ncol=n.cats)
     bpar <- matrix( c( rep( 0, n.covs), c( -0.9, 0.5), c( 0.8, -0.4), c( -0.4, -0.7)), nrow=n.covs, ncol=n.cats)
     chain.ns <- sim.chain( n.chains=nc, n.obs=rep( n, nc), n.cats=n.cats, n.covars=n.covs, beta=bpar, gamma=gpar, X=X)
     fm.est1 <- RMC.mod( states=chain.ns[,2], chain.id=chain.ns[,1], X=chain.ns[,3:4], phi.id=my.phi.id, pi.id=my.pi.id)
     temp1 <- diagnos.envel( chain.id=chain.ns[,1], obs.states=chain.ns[,2], X=chain.ns[,3:4], fit=fm.est1, B=B)
     hrplot( temp1[["patch"]][[my.cat]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Non-Stationary Data, stat ... [TRUNCATED]
     hrplot( temp1[["patch"]][[my.cat]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Non-Stationary Data, stat ... [TRUNCATED]
     hrplot( temp1$movement, ylab="Movement Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Non-Stationary Data", pch=20)
     chain.orig <- sim.chain( n.chains=nc, n.obs=rep( n, nc), n.cats=n.cats, n.covars=1, beta=c(0,0.3,-0.3,0), gamma=c(0.5,0.2,1,0))
     temp1 <- diagnos.envel( chain.id=chain1[,1], obs.state=chain1[,2], X=chain1[,3,drop=FALSE], fm1, B=B)
     temp2 <- diagnos.envel( chain.id=chain2[,1], obs.state=chain2[,2], X=chain2[,3,drop=FALSE], fm2, B=B)
     hrplot( temp1[["patch"]][[3]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Data with Patch Outlier", pch= ... [TRUNCATED]
     hrplot( temp2$movement, ylab="Movement Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Data with Repetition Outlier", pch ... [TRUNCATED]
     gpar <- matrix( c( c( -0.6, 0), c( 1.1, 1.5), c( 0.2, 0), c( -1.25, -1.5)), nrow=n.covs, ncol=n.cats)
     bpar <- matrix( c( rep( 0, n.covs), c( -0.9, 0.5), c( 0.8, -0.4), c( -0.4, -0.7)), nrow=n.covs, ncol=n.cats)
     chain <- sim.chain( n.chains=nc, n.obs=rep( n, nc), n.cats=n.cats, n.covars=n.covs, beta=bpar, gamma=gpar, X=X)
     fm.est1 <- RMC.mod( states=chain[,2], chain.id=chain[,1], X=chain[,3:4], phi.id=my.phi.id, pi.id=my.pi.id)
     temp <- diagnos.envel( chain.id=chain[,1], obs.states=chain[,2], X=chain[,3,drop=FALSE], fit=fm.est, B=B)
     temp1 <- diagnos.envel( chain.id=chain[,1], obs.states=chain[,2], X=chain[,-(1:2)], fit=fm.est1, B=B)
     hrplot( temp[["patch"]][[my.set[1]]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Mean Only Model", pch=2 ... [TRUNCATED]
     hrplot( temp[["patch"]][[my.set[2]]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Mean Only Model", pch=2 ... [TRUNCATED]
     hrplot( temp$movement, ylab="Movement Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Mean Only Model", pch=20)
     hrplot( temp1[["patch"]][[my.set[1]]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Correct Model", pch=20 ... [TRUNCATED]
     hrplot( temp1[["patch"]][[my.set[2]]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Correct Model", pch=20 ... [TRUNCATED]
     hrplot( temp1$movement, ylab="Movement Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Correct Model", pch=20)

Rd file 'hrplot.Rd':
  \examples lines wider than 100 characters:
     temp.est <- diagnos.envel( obs.states=dataEG1[,2], chain.id=dataEG1[,1], X=dataEG1[,3,drop=FALSE], fit=fm.est, B=B)
     hrplot( temp.est[["patch"]][[2]], ylab="Patch Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Stationary Data", pch=20)
     hrplot( temp.est$movement, ylab="Movement Residuals - Normal Quantiles", xlab="Normal Quantiles", main="Stationary Data", pch=20)

Rd file 'sim.chain.Rd':
  \usage lines wider than 90 characters:
     sim.chain( n.chains = 5, n.obs = rep( 100, n.chains), n.cats = 3, n.covars = 1, beta = NULL, gamma = NULL, X = NULL)
  \examples lines wider than 100 characters:
     #simulate 3 chains with 4 states of length 1500. There is one (constant) covariate. The beta parameters are for the pi probabilities an ... [TRUNCATED]
     chain <- sim.chain( n.chains=3, n.obs=rep( 1500, 3), n.cats=4, n.covars=1, beta=c(0,0.3,-0.3,0), gamma=c(0.5,0.2,1,0))
      plot( 1:100, tmpDat[1:100,"state"], main=paste( "Chain", ii), ylab="State", xlab="Index", type='b', pch=20)

Rd file 'simRandWalk.Rd':
  \examples lines wider than 100 characters:
     plot( 1:ni[1], simDat[1:ni[1],2], type='b', pch=20, ylab="Random Variable", xlab="Index", main="Chain 1")
     plot( 1:ni[2], simDat[ni[1]+1:ni[2],2], type='b', pch=20, ylab="Random Variable", xlab="Index", main="Chain 2")
     plot( 1:ni[3], simDat[sum(ni[1:2])+1:ni[3],2], type='b', pch=20, ylab="Random Variable", xlab="Index", main="Chain 3")

These lines will be truncated in the PDF manual.