ocpt.np.update: Identifying Changes Via ECP. Update function.
In rkillick/changepoint.online: Online detection of changepoints

Description Usage Arguments Details Value Author(s) 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 e.cp3o_delta.online.initialise has created an answer.

1	ocpt.np.update(previousanswer, newdata,K)

`previousanswer`	A S4 class. This will be the output from the `e.cp3o_delta.online.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.
`K`	Maximum number of changpoints the algorithm will output as estimates. Also the total number of iterations the dynamic programming will do.

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

A ecp.ocpt object is returned with a summary:

`number of changepoints`	The estimated number of change points.
`Estimate Locations`	The location of the change points estimated by the procedure.
`Goodness of Fit Model`	Goodness of Fit Model's score.
`Delta`	The window size.
`Alpha`	The moment index used.
`Verbose`	A flag indicating if status updates should be printed.
`Number of Datapoints`	Total number of data pieces observed.
`Calcualtion Time`	Time procedure took.

Andrew Connell, Rebecca Killick, David Matteson

ocpt.var.initialise,ocpt.meanvar.initialise,ocpt

set.seed(1)
x = matrix(c(rnorm(100,50,1),rnorm(100,5,1)),ncol=1)
y=matrix(c(rnorm(100,15,1),rnorm(100,25,1)),ncol=1)
ans = ocpt.np.initialise(x)
updatedans = ocpt.np.update(ans,y,K=3)
updatedans