crops: Generic implementation of the crops algorithm (ref goes...

In grosed/crops: Changepoints for a Range of Penalties (CROPS)

Generic implementation of the crops algorithm (ref goes here).

Description

Provides a generic implementation of the crops (changepoints for a range of penalties) algorithm of Haynes et al. (2014) which efficiently searches a range of penalty values in multiple changepoint problems. The crops algorithm finds the optimal segmentations for a different number of segments without incurring as large a computational cost as solving the constrained optimisation problem for a range of values for the number of changepoints. To make the method generic, the user must provide a function that maps a penalty value to the results obtained by a penalised cost changepoint method, and formats these results in a specific way. This interface to the generic method is similar to that as used by the optimx package.

Usage

crops(method, beta_min, beta_max, max_iterations = 20, ...)

Arguments

method	A function mapping a penalty value to the results obtained by a penalised cost changepoint method. The function must return a list containing the cost and a vector of changepoint locations corresponding to the optimal segmentation as determined by a penalised cost changepoint method.
beta_min	A positive numeric value indicating the smallest penalty value to consider.
beta_max	A positive numeric value indicating the maximum penalty value to consider.
max_iterations	Positive non zero integer. Limits the maximum number of iterations of the crops algorithm to max_iterations. Default value is max_iterations=20
...	Additional parameters to pass to the underlying changepoint method if required.

Value

An instance of an S4 class of type crops.class.

References

\insertRef

crops-articlecrops

\insertRef

optimx-1crops

\insertRef

optimx-2crops

\insertRef

optimx-packagecrops

\insertRef

fpop-articlecrops

\insertRef

fpop-packagecrops

Examples

# generate some simple data
set.seed(1)
N <- 100
data.vec <- c(rnorm(N), rnorm(N, 2), rnorm(N))

# example one - calling fpop via crops using global scope
# need the fpop library
library(pacman)
p_load(fpop)
# create a function to wrap a call to fpop for use with crops
fpop.for.crops<-function(beta)
    {
       # Note - this code is taken from the example in the fpop package
       fit <- Fpop(data.vec, beta)
       end.vec <- fit$t.est
       change.vec <- end.vec[-length(end.vec)]
       start.vec <- c(1, change.vec+1)
       segs.list <- list()
       for(seg.i in seq_along(start.vec))
           {
            start <- start.vec[seg.i]
            end <- end.vec[seg.i]
            seg.data <- data.vec[start:end]
            seg.mean <- mean(seg.data)
            segs.list[[seg.i]] <- data.frame(
                                    start, end,
                                    mean=seg.mean,
                                    seg.cost=sum((seg.data-seg.mean)^2))
            }
        segs <- do.call(rbind, segs.list)
        return(list(sum(segs$seg.cost),segs$end[-length(segs$end)]))
    }

# now use this wrapper function with crops
res<-crops(fpop.for.crops,0.5*log(300),2.5*log(300))
# print summary of analysis
summary(res)
# summarise the segmentations
segmentations(res)
# visualise the segmentations
plot(res)
# overlay the data on the segmentations
df <- data.frame("x"=1:300,"y"=data.vec)
plot(res,df) 

grosed/crops documentation built on Aug. 8, 2022, 4:29 a.m.