TED (version 1.1.1)

plotevents: Plot the detected events

Description

This function plots the detected events from a time series.

Usage

plotevents(events, cluster = FALSE, mycl, ...)

Arguments

events

an object of class `events'.

cluster

logical, if TRUE then the detected events are highlighted using different colors for different clusters

mycl

a vector specifying which cluster each event belongs to

...

other arguments that can be passed to plot

Value

...

References

Yanfei Kang, Danijel Belusic and Kate Smith-Miles (2014). Detecting and Classifying Events in Noisy Time Series. J. Atmos. Sci., 71, 1090-1104. http://dx.doi.org/10.1175/JAS-D-13-0182.1.

See Also

noiseTests, eventExtraction, eventDetection

Examples

##################################
#   1st art eg (white noise)
##################################
set.seed(123)
n=128
types=c('box','rc','cr','sine')
shapes=matrix(NA,20,n)
for (i in 1:20){
  shapes[i,]=cbfs(type=types[sample(1:4,1)])
}
whitenoise=ts2mat(rnorm(128*20),128)
# generate x which randomly combine the four types of events with each two of them
# separated by noise
x=c(rnorm(128),t(cbind(shapes,whitenoise)))
plot(x,ty='l')
w=128; alpha=0.05
# event detection
## Not run: 
# events=eventDetection(x,w,'white',FALSE,alpha,'art')
# clustering events
# cc=eventCluster(events,4)
# myclkm=cc$cl
# # plot the clustered events
# plotevents(events,cluster=TRUE, myclkm)
# ## End(Not run)
##################################
#   2nd art eg (red noise)
##################################
set.seed(123)
# generate a time series with red noise; this is the same with the one used
# in Kang et al. (2014)
coeff=0.5;s=1
x=c(arima.sim(list(order = c(1,0,0),ar=coeff),n=500,sd=s),
    cbfs_red('rc'),arima.sim(list(order = c(1,0,0),ar=coeff),n=400,sd=s),
    cbfs_red('cr'),arima.sim(list(order = c(1,0,0),ar=coeff),n=400,sd=s),
    cbfs_red('box'),arima.sim(list(order = c(1,0,0),ar=coeff),n=400,sd=s),
    cbfs_red('sine'),arima.sim(list(order = c(1,0,0),ar=coeff),n=1000,sd=s),
    arima.sim(list(order = c(1,0,0),ar=0.8),n=1100,sd=4))
w=128; alpha=0.05
# event detection
## Not run: 
# events=eventDetection(x,w,'red',parallel=FALSE,alpha,'art')
# # plot events without clustering
# plotevents(events)
# ## End(Not run)