Description Usage Arguments Value Author References Examples
View source: R/detectFFFMeanCDR.R
Given a vector x
, use the FFF method to sequentially detect changes
(or a single change) in the MEAN of the vector.
1 2 3 
x 
The vector (stream) in which to detect change(s). 
lambda 
The value for the forgetting factor.
Default is 
alpha 
The value for the threshold. Default is 
BL 
The burnin length. Default is 
multiple 
Boolean to use to decide whether to detect multiple changes
or only a single change. Default is 
single 
Boolean to use to decide whether to detect only a single
change or multiple changes. Set to 
usePrechange 
Boolean indicating whether prechange parameters
(mean and variance) are known and will be used
(or not). Default is

prechangeMean 
Value to be used for the prechange mean.
Default is 
prechangeSigma 
Value to be used for the prechange standard
deviation. Default is 
prechangeVar 
Value to be used for the prechange variance.
Default is 
skipCheck 
A boolean which allows the function to skip the check
of the stream. Default is 
A list with the following elements:
tauhat
A vector of the changepoints found.
Dean Bodenham
D. A. Bodenham and N. M. Adams (2016) Continuous monitoring for changepoints in data streams using adaptive estimation. Statistics and Computing doi:10.1007/s1122201696848
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # create a stream with three changepoints
set.seed(8)
x < rnorm(400, 5, 1) + rep(c(0:3), each=100) # mean is 5 and s.d. is 1
# multiple changepoints
list_fff < detectFFFMean(x, alpha=0.01, lambda=0.95, BL=50, multiple=TRUE)
# now only a single (the first) changepoint
list_fff2 < detectFFFMean(x, alpha=0.01, lambda=0.95, BL=50, single=TRUE)
# now only a single (the first) changepoint, but with the prechange
# mean and variance known
list_fff3 < detectFFFMean(x, alpha=0.01, lambda=0.95, BL=50, single=TRUE,
prechangeMean=5, prechangeSigma=1)

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