number of bootstrap samples used in the bootstrap restarting algorithm. If 0 the standard algorithm, i.e. without bootstrap restart, is used. Default to 10 that appears to be sufficient in most of problems. However when multiple breakpoints have to be estimated it is suggested to increase n.boot, e.g. n.boot=50.

logical indicating if the value of the objective function should be printed (along with current breakpoint estimates) at each iteration or at each bootstrap resample. If bootstrap restarting is employed, the values of objective and breakpoint estimates should not change at the last runs.

positive convergence tolerance.

integer giving the maximal number of iterations.

logical (it replaces previous argument stop.if.error) If TRUE (default) the number (and not location) of breakpoints is held fixed throughout iterations. Otherwise a sort of ‘automatic’ breakpoint selection is carried out, provided that several starting values are supplied for the breakpoints, see argument psi in segmented.lm or segmented.glm. The idea, relying on removing the ‘non-admissible’ breakpoint estimates at each iteration, is discussed in Muggeo and Adelfio (2011) and it is not compatible with the bootstrap restart algorithm. fix.npsi=FALSE, indeed, should be considered as a preliminary and tentative approach to deal with an unknown number of breakpoints. Alternatively, see selgmented.

the number of quantiles (or equally-spaced values) to supply as starting values for the breakpoints when the psi argument of segmented is set to NA. K is ignored when psi is different from NA.

logical, indicating how the starting values should be selected. If FALSE equally-spaced values are used, otherwise the quantiles. Ignored when psi is different from NA.

integer giving the maximum number of inner IWLS iterations (see details).

positive factor modifying the increments in breakpoint updates during the estimation process (see details).

Integer, less than n.boot. If break.boot consecutive bootstrap samples lead to the same objective function, the algorithm stops without performing all n.boot 'trials'. This can save computational time considerably.

the size of the bootstrap samples. If NULL, it is taken equal to the actual sample size.

logical. If TRUE the values of the segmented variable(s) are jittered before fitting the model to the bootstrap resamples.

if TRUE nonparametric bootstrap (i.e. case-resampling) is used, otherwise residual-based. Currently working only for LM fits. It is not clear what residuals should be used for GLMs.

if TRUE, when the algorithm fails to obtain a solution, random values are employed to obtain candidate values.

The seed to be passed on to set.seed() when n.boot>0. Set to NULL to use a random seed. Fixing the seed can be useful to replicate the results when the bootstrap restart algorithm is employed. The segmented fit includes seed representing the integer vector saved just before the bootstrap resampling. Re-use it if you want to replicate the bootstrap restarting algorithm with the same samples.

A character string to be used (optionally) only when segmented.default is used. It represents the function (with argument 'x') to be applied to the fit object to extract the objective function to be minimized. Thus for "lm" fits (although unnecessary) it should be fn.obj="sum(x$residuals^2)", for "coxph" fits it should be fn.obj="-x$loglik[2]". If NULL the ‘minus log likelihood’ extracted from the object, namely "-logLik(x)", is used. See segmented.default.

optional. If specified it means the desidered number of decimal points of the breakpoint to be used during the iterative algorithm.

optional. Should convergence of iterative procedure to be assessed on changes of breakpoint estimates or changes in the objective? Default to FALSE.

optional numerical value. The breakpoint is estimated within the quantiles alpha and 1-alpha of the relevant covariate.

optional. The minimum step size to break the iterative algorithm. Default to 0.0001.

The powers of the pseudo covariates employed by the algorithm. These can be altered during the iterative process to stabilize the estimation procedure. Usually of no interest for the user. This argument will be removed in next releases.

logical indicating if output should include only the last fitted model. This argument will be removed in next releases

same than fix.npsi. This argument will be removed in next releases, and replaced by fix.npsi. If provided, and different from NULL, it overwrites fix.npsi

logical, if FALSE the gap coefficients are always constrained to zero at the convergence. This argument will be removed in next releases.

A proportionality factor (<= 1) to adjust the breakpoint estimates if these come close to the boundary or too close each other. For instance, if psi turns up close to the maximum, it will be changed to psi*fc or to psi/fc if close to the minimum. This is useful to get finite point estimate and standard errors for each slope parameter.