Description Usage Arguments Examples
View source: R/LLE_functions.R
Function to plot the Local Lyapunov Exponents. The output is of class ggplot2 so you can add standard ggplot2 options to it if desired.
1 | plotLLE(LLE)
|
LLE |
The output from TSIR_LLE |
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require(kernlab)
require(ggplot2)
require(kernlab)
London <- twentymeas$London
## just analyze the biennial portion of the data
London <- subset(London, time > 1950)
## define the interval to be 2 weeks
IP <- 2
## first estimate paramters from the London data
parms <- estpars(data=London, IP=2, regtype='gaussian',family='poisson',link='log')
## look at beta and alpha estimate
plotbeta(parms)
## simulate the fitted parameters
sim <- simulatetsir(data=London,parms=parms,IP=2,method='deterministic',nsim=2)
## now lets predict forward 200 years using the mean birth rate,
## starting from rough initial conditions
times <- seq(1965,2165, by = 1/ (52/IP))
births <- rep(mean(London$births),length(times))
S0 <- parms$sbar
I0 <- 1e-5*mean(London$pop)
pred <- predicttsir(times=times,births=births,
beta=parms$contact$beta,alpha=parms$alpha,
S0=S0,I0=I0,
nsim=50,stochastic=T)
## take the last 10 years
pred <- lapply(pred, function(x) tail(x, 52/IP * 20) )
## now compute the Lyapunov Exponent for the simulate and predicted model
simLE <- TSIR_LE(
time=sim$res$time,
S=sim$simS$mean,
I=sim$res$mean,
alpha=sim$alpha,
beta=sim$contact$beta,
IP=IP
)
predLE <- TSIR_LE(
time=pred$I$time,
S=pred$S$X3,
I=pred$I$X3,
alpha=parms$alpha,
beta=parms$contact$beta,
IP=IP
)
simLE$LE
predLE$LE
simLLE <- TSIR_LLE(simLE)
predLLE <- TSIR_LLE(predLE)
plotLLE(simLLE)
plotLLE(predLLE)
## End(Not run)
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