# NOT RUN {
# Example of a change in scale parameter (mean and variance) at 100 in simulated gamma data
set.seed(1)
x=c(rgamma(100,shape=1,rate=1),rgamma(100,shape=1,rate=5))
cpt.meanvar(x,penalty="SIC",method="AMOC",test.stat="Gamma",class=FALSE,shape=1) # returns 97 to
#show that the null hypothesis was rejected and the change in scale parameter is at 97
ans=cpt.meanvar(x,penalty="AIC",method="AMOC",test.stat="Gamma",shape=1)
cpts(ans)
# returns 97 to show that the null hypothesis was rejected, the change in scale parameter is at 97
# Example of multiple changes in mean and variance at 50,100,150 in simulated normal data
set.seed(1)
x=c(rnorm(50,0,1),rnorm(50,5,3),rnorm(50,10,1),rnorm(50,3,10))
cpt.meanvar(x,penalty="Manual",pen.value="4*log(n)",method="BinSeg",Q=5,class=FALSE)
# returns optimal number of changepoints is 4, locations are 50,100,150,152.
# Example of using the CROPS penalty in the above example
set.seed(1)
x=c(rnorm(50,0,1),rnorm(50,5,3),rnorm(50,10,1),rnorm(50,3,10))
out=cpt.meanvar(x,pen.value=c(2*log(length(x)),100*log(length(x))),penalty="CROPS",method="PELT")
cpts.full(out)
# returns 6 segmentations for penalty values between 2log(n) and 100log(n).
# We find segmentations with 9, 7, 4, 3, 1 and 0 changepoints.
# Note that the empty final row indicates no changepoints.
pen.value.full(out) # gives associated penalty transition points
# CROPS does not give an optimal set of changepoints thus we may wish to explore further
plot(out,diagnostic=TRUE)
# looks like the segmentation with 4 changepoints, 50,100,150,200 is the most appropriate
plot(out,ncpts=3)
# Example multiple datasets where the first row has multiple changes in mean and variance and the
#second row has no change in mean or variance
set.seed(1)
x=c(rnorm(50,0,1),rnorm(50,5,3),rnorm(50,10,1),rnorm(50,3,10))
y=rnorm(200,0,1)
z=rbind(x,y)
cpt.meanvar(z,penalty="Asymptotic",pen.value=0.01,method="SegNeigh",Q=5,class=FALSE) # returns list
#that has two elements, the first has 3 changes in mean and variance at 50,100,150 and the second
#has no changes in mean or variance
ans=cpt.meanvar(z,penalty="Asymptotic",pen.value=0.01,method="PELT")
cpts(ans[[1]]) # same results as for the SegNeigh method.
cpts(ans[[2]]) # same results as for the SegNeigh method.
# }
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