# seg.control

0th

Percentile

##### Auxiliary for controlling segmented model fitting

Auxiliary function as user interface for 'segmented' fitting. Typically only used when calling any 'segmented' method (segmented.lm, segmented.glm, segmented.Arima or segmented.default).

Keywords
regression
##### Usage
seg.control(n.boot=10, display = FALSE, tol = 1e-05, it.max = 30, fix.npsi=TRUE,
K = 10, quant = TRUE, maxit.glm = 25, h = 1, size.boot=NULL, jt=FALSE, nonParam=TRUE,
random=TRUE, seed=NULL, fn.obj=NULL, digits=NULL, conv.psi=FALSE,
alpha=.02, min.step=.0001,
powers=c(1,1), last = TRUE, stop.if.error = NULL, gap=FALSE)
##### Arguments
n.boot

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.

display

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.

tol

positive convergence tolerance.

it.max

integer giving the maximal number of iterations.

fix.npsi

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.

K

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.

quant

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.

maxit.glm

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

h

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

size.boot

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

jt

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

nonParam

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.

random

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

seed

The seed to be passed on to set.seed() when n.boot>0. Setting the seed can be useful to replicate the results when the bootstrap restart algorithm is employed. In fact a 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.

fn.obj

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.

digits

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

conv.psi

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

alpha

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

min.step

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

powers

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.

last

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

stop.if.error

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

gap

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

##### Details

Fitting a segmented' GLM model is attained via fitting iteratively standard GLMs. The number of (outer) iterations is governed by it.max, while the (maximum) number of (inner) iterations to fit the GLM at each fixed value of psi is fixed via maxit.glm. Usually three-four inner iterations may be sufficient.

When the starting value for the breakpoints is set to NA for any segmented variable specified in seg.Z, K values (quantiles or equally-spaced) are selected as starting values for the breakpoints. In this case, it may be useful to set also fix.npsi=FALSE to automate the procedure, see Muggeo and Adelfio (2011). The maximum number of iterations (it.max) should be also increased when the automatic' procedure is used.

If last=TRUE, the object resulting from segmented.lm (or segmented.glm) is a list of fitted GLM; the i-th model is the segmented model with the values of the breakpoints at the i-th iteration.

Since version 0.2-9.0 segmented implements the bootstrap restarting algorithm described in Wood (2001). The bootstrap restarting is expected to escape the local optima of the objective function when the segmented relationship is flat. Notice bootstrap restart runs n.boot iterations regardless of tol that only affects convergence within the inner loop.

##### Value

A list with the arguments as components.

##### References

Muggeo, V.M.R., Adelfio, G. (2011) Efficient change point detection in genomic sequences of continuous measurements. Bioinformatics 27, 161--166.

Wood, S. N. (2001) Minimizing model fitting objectives that contain spurious local minima by bootstrap restarting. Biometrics 57, 240--244.

• seg.control
##### Examples
# NOT RUN {
#decrease the maximum number inner iterations and display the
#evolution of the (outer) iterations
seg.control(display = TRUE, maxit.glm=4)

# }
`
Documentation reproduced from package segmented, version 1.0-0, License: GPL

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