ctsem (version 3.9.1)

isdiag: Diagnostics for ctsem importance sampling

Description

Diagnostics for ctsem importance sampling

Usage

isdiag(fit)

Value

Nothing. Plots convergence of parameter mean estimates from initial Hessian based distribution to final sampling distribution.

Arguments

fit

Output from ctStanFit when optimize=TRUE and isloops > 0

Examples

# \donttest{
#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=0, 
  optimcontrol=list(is=TRUE, finishsamples=500),priors=TRUE)
isdiag(fit)
# }