Diagnostics for ctsem importance sampling
Diagnostics for ctsem importance sampling
isdiag(fit)
fit |
Output from ctStanFit when optimize=TRUE and isloops > 0 |
Nothing. Plots convergence of parameter mean estimates from initial Hessian based distribution to final sampling distribution.
if(w32chk()){ #get data sunspots<-sunspot.year sunspots<-sunspots[50: (length(sunspots) - (1988-1924))] id <- 1 time <- 1749:1924 datalong <- cbind(id, time, sunspots) #setup model model <- ctModel(type='stanct', manifestNames='sunspots', latentNames=c('ss_level', 'ss_velocity'), LAMBDA=matrix(c( -1, 'ma1 | log(exp(-param)+1)' ), nrow=1, ncol=2), DRIFT=matrix(c(0, 'a21', 1, 'a22'), nrow=2, ncol=2), MANIFESTMEANS=matrix(c('m1 | (param)*5+44'), nrow=1, ncol=1), CINT=matrix(c(0, 0), nrow=2, ncol=1), T0VAR=matrix(c(1,0,0,1), nrow=2, ncol=2), #Because single subject DIFFUSION=matrix(c(0.0001, 0, 0, "diffusion"), ncol=2, nrow=2)) #fit and plot importance sampling diagnostic fit <- ctStanFit(datalong, model,verbose=1, optimcontrol=list(is=TRUE, finishsamples=500),nopriors=FALSE) isdiag(fit) }
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