ocpt.meanvar.update: Identifying Changes in Mean and variance. Update function.
In rkillick/changepoint.online: Online detection of changepoints

Description Usage Arguments Details Value Author(s) References See Also Examples

Calculates the optimal positioning and (potentially) number of changepoints for data using the user specified method. The update function should be used once ocpt.meanvar.initialise has created an answer.

1	ocpt.meanvar.update(previousanswer, newdata)

`previousanswer`	A S4 class. This will be the output from the `ocpt.mean.initialise` function.
`newdata`	A vector, ts object or matrix containing the new data within which you wish to find a changepoint. If data is a matrix, each row is considered a separate dataset.

This function is used to find changes in mean for data using the test statistic specified in the test.stat parameter. The changes are found using the method supplied which can be single changepoint or multiple changepoints. A changepoint is denoted as the last observation of the segment / regime.

If class=TRUE then an object of S4 class "ocpt" is returned. The slot ocpts contains the changepoints that are returned. For class=FALSE the structure is as follows.

If data is a vector (single dataset) then a vector/list is returned depending on the value of method. If data is a matrix (multiple datasets) then a list is returned where each element in the list is either a vector or list depending on the value of method.

Returns a summary of:

`cpttype`	In this case it will always be change in mean.
`Method`	The method used.
`test.stat`	The chosen test statistic.
`penalty`	Both the type and value of the penalty
`minseglen`	The minimum segment length.
`max no. of cpts`	Maximum number of changepoints possible.
`ndone`	Length of the time series when analysis begins.
`nupdate`	Length of the time series to be analysed in this update.

Andrew Connell Rebecca Killick

Change in Normal mean: Hinkley, D. V. (1970) Inference About the Change-Point in a Sequence of Random Variables, Biometrika 57, 1–17

PELT Algorithm: Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost, JASA 107(500), 1590–1598

MBIC: Zhang, N. R. and Siegmund, D. O. (2007) A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data. Biometrics 63, 22-32.

ocpt.var.initialise,ocpt.mean.initialise,ocpt

set.seed(1)
x=c(rnorm(50,0,1),rnorm(50,5,1),rnorm(50,10,1),rnorm(50,3,1))
y = c(rnorm(50,15,1),rnorm(50,25,1),rnorm(50,33,1),rnorm(50,7,1))
previousans = ocpt.meanvar.initialise(x)
ocpt.meanvar.update(previousanswer = previousans, newdata = y)

x_ecp = matrix(c(rnorm(50,0,1),rnorm(50,5,1),rnorm(50,10,1),rnorm(50,3,1)), ncol = 1)
y_ecp = matrix(c(rnorm(50,15,1),rnorm(50,25,1),rnorm(50,33,1),rnorm(50,7,1)), ncol = 1)
previousansecp = ocpt.meanvar.initialise(x_ecp, test.stat="ECP")
ocpt.meanvar.update(previousanswer = previousansecp, newdata = y_ecp)