LinearOsc: Simulated time series data for a deterministic linear damped...

In dynr: Dynamic Models with Regime-Switching

Simulated time series data for a deterministic linear damped oscillator model

Description

The variables are as follows:

Usage

data(LinearOsc)

Format

A data frame with 1000 rows and 3 variables

Details

• ID. ID of the systems (1 to 10)

• x. Latent level variable

• theTimes. Measured time Points

Examples

# The following was used to generate the data
#--------------------------------------
## Not run: 
Osc <- function(t, prevState, parms) {
  x1 <- prevState[1] # x1[t]
  x2 <- prevState[2] # x2[t]
  eta1 = parms[1]
  zeta1 = parms[2]
  with(as.list(parms), {
   dx1 <- x2
    dx2 <- eta1*x1 + zeta1*x2 
    res<-c(dx1,dx2)
    list(res)
  }
  )
}
n = 10 #Number of subjects
T = 100 #Number of time points
deltaT = .1 #dt
lastT = deltaT*T #Value of t_{i,T}
theTimes  = seq(0, lastT, length=T)  #A list of time values

eta = -.8
zeta = -.1
out1 = matrix(NA,T*n,1)
trueOut = matrix(NA,T*n,1)
parms = c(eta, zeta)
  for (i in 1:n){
  xstart = c(rnorm(1,0,2),rnorm(1,0,.5))
  out <- lsoda(as.numeric(xstart), theTimes, Osc, parms)
  trueOut[(1+(i-1)*T):(i*T)] = out[,2]
  out1[(1+(i-1)*T):(i*T)] = out[,2]+rnorm(T,0,1)
  }

LinearOsc= data.frame(ID=rep(1:n,each=T),x=out1[,1],
                  theTimes=rep(theTimes,n))
save(LinearOsc,file="LinearOsc.rda")

## End(Not run)

dynr documentation built on Oct. 17, 2022, 9:06 a.m.