dpseg_old: inefficient 'dpseg' implementation
In dpseg: Piecewise Linear Segmentation by Dynamic Programming

Description Usage Arguments Details Value Examples

See dpseg for a current version of this algorithm. Note: this was a first test implementation of the linear piecewise segmentation by a dynamic programming approach. This implementation is very slow. A much more efficient version, dpseg, calculates the variance of residuals of a linear regression incrementally while looping through the recursion, and is implemented in Rcpp. See there for details on the algorithm. This version is kept alive, since it is a more general implementation, allowing to test different regression and scoring functions by command-line arguments.

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dpseg_old(x, y, minl, maxl = length(x), P = 0, EPS,
  store.matrix = FALSE, fitscoref = fitscore, fitf = linregf,
  scoref = varscore, verb = 0)

`x`	x-values
`y`	y-values
`minl`	minimal segment length
`maxl`	maximal segment length
`P`	jump penalty, increase to get fewer segments # @inheritParams score
`EPS`	a pre-calculated fitscore matrix, will be generated if missing
`store.matrix`	store the fitscore matrix
`fitscoref`	the heavy-load loop that fills the fitscore matrix using `fitf` and `scoref`
`fitf`	fit function, used in the scoring function `scoref`; (TODO: currently expecting a fit object that provides intercept and slope as `coef(obj)[1:2]` only for the result table)
`scoref`	function to calculate a score from the passed fit function
`verb`	print progress messages

The recursion calculates S_j = max ( S_i + fitscore(i+1,j)) - P, where the fitscore is the variance of the residuals of a linear regression (lm(y~x)) between x_{i+1} to x_j, P is a jump penality that implicitly regulates the number of segments, minl and maxl are minimal and maximal lengths of segments. Uses RcppEigen:fastLm for linear regression.

Returns a list of result structures very similar to the list of class "dpseg" returned by function dpseg, except for the name of the scoring function matrix, here: EPS. See ?dpseg for detailed information on these structures.

## NOTE: not run because it's too slow for R CMD check --as-cran
## calculate linear segments in semi-log bacterial growth data
## NOTE: library loads bacterial growth curve data as data.frame oddata
Sj <- dpseg_old(x=oddata$Time, y=log(oddata$A3), minl=5, P=0.0001, verb=1)

## inspect resulting segments
print(Sj)

## plot results
plot(Sj, delog=TRUE, log="y")

## NOTE: predict method & movie function do not work for dpseg_old