isdiag: Diagnostics for ctsem importance sampling In ctsem: Continuous Time Structural Equation Modelling

Description

Diagnostics for ctsem importance sampling

Usage

 `1` ```isdiag(fit) ```

Arguments

 `fit` Output from ctStanFit when optimize=TRUE and isloops > 0

Value

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

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```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=0, optimcontrol=list(is=TRUE, finishsamples=500),nopriors=FALSE) isdiag(fit) } ```

ctsem documentation built on July 23, 2021, 5:07 p.m.