# stepbound: Jump estimation under restrictions In stepR: Multiscale Change-Point Inference

## Description

Computes piecewise constant maximum likelihood estimators with minimal number of jumps under given restrictions on subintervals.

Deprecation warning: This function is a help function for `smuceR` and `jsmurf` and may be removed when these function will be removed.

## Usage

 ```1 2 3 4 5 6 7 8``` ```stepbound(y, bounds, ...) ## Default S3 method: stepbound(y, bounds, x = 1:length(y), x0 = 2 * x - x, max.cand = NULL, family = c("gauss", "gaussvar", "poisson", "binomial", "gaussKern"), param = NULL, weights = rep(1, length(y)), refit = y, jumpint = confband, confband = FALSE, ...) ## S3 method for class 'stepcand' stepbound(y, bounds, refit = TRUE, ...) ```

## Arguments

 `y` a vector of numerical observations `bounds` bounds on the value allowed on intervals; typically computed with `bounds` `x` a numeric vector of the same length as `y` containing the corresponding sample points `x0` a single numeric giving the last unobserved sample point directly before sampling started `max.cand, weights` see `stepcand` `family, param` specifies distribution of data, see family `refit` `logical`, for `family = "gaussKern"`; determines whether a fit taken the filter kernel into account will be computed at the end `jumpint` `logical` (`FALSE` by default), indicates if confidence sets for jumps should be computed `confband` `logical`, indicates if a confidence band for the piecewise-continuous function should be computed `...` arguments to be passed to generic methods

## Value

An object of class `stepfit` that contains the fit; if `jumpint == TRUE` function `jumpint` allows to extract the confidence interval for the jumps, if `confband == TRUE` function `confband` allows to extract the confidence band.

## References

Frick, K., Munk, A., and Sieling, H. (2014) Multiscale change-point inference. With discussion and rejoinder by the authors. Journal of the Royal Statistical Society, Series B 76(3), 495–580.

Hotz, T., Schütte, O., Sieling, H., Polupanow, T., Diederichsen, U., Steinem, C., and Munk, A. (2013) Idealizing ion channel recordings by a jump segmentation multiresolution filter. IEEE Transactions on NanoBioscience 12(4), 376–386.

`bounds`, `smuceR`, `jsmurf`, `stepsel`, `stepfit`, `jumpint`, `confband`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# simulate poisson data with two levels y <- rpois(100, c(rep(1, 50), rep(4, 50))) # compute bounds b <- bounds(y, penalty="len", family="poisson", q=4) # fit step function to bounds sb <- stepbound(y, b, family="poisson", confband=TRUE) plot(y) lines(sb) # plot confidence intervals for jumps on axis points(jumpint(sb), col="blue") # confidence band lines(confband(sb), lty=2, col="blue") ```