segmented (version 0.5-1.1)

seg.control: Auxiliary for controlling segmented model fitting

Description

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

Usage

seg.control(toll = 1e-04, it.max = 10, display = FALSE, stop.if.error = TRUE, 
    K = 10, quant = FALSE, last = TRUE, maxit.glm = 25, h = 1, 
    n.boot=20, size.boot=NULL, gap=FALSE, jt=FALSE, nonParam=TRUE,
    random=TRUE, powers=c(1,1), seed=NULL, fn.obj=NULL)

Arguments

toll

positive convergence tolerance.

it.max

integer giving the maximal number of iterations.

display

logical indicating if the value of the working objective function should be printed at each iteration. The working objective function is the objective function of the working model including the gap coefficients (and therefore

stop.if.error

logical indicating if non-admissible break-points should be removed during the estimating algorithm. Set it to FALSE if you want to perform a sort of `automatic' breakpoint selection, provided that several starting values are provi

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 diff

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.

last

logical indicating if output should include only the last fitted model.

maxit.glm

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

h

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

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 20 that appears to be sufficient in most of problems. However when multiple breakpoints have

size.boot

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

gap

logical, if FALSE the gap coefficients are always constrained to zero at the convergence.

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.

powers

The powers of the pseudo covariates employed by the algorithm. These are possibly altered during the iterative process to stabilize the estimation procedure. Usually of no interest for the user.

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

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

Value

• A list with the arguments as components.

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 stop.if.error=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. Sometimes to stabilize the procedure, it can be useful to set h<1< code=""> to reduce the increments in the breakpoint updates. At each iteration the updated estimate is usually given by psi.new=psi.old+increm. By setting h<1< code=""> (actually min(abs(h),1) is considered) causes the following updates of the breakpoint estimate: psi.new=psi.old+h*increm. 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 toll that only affects convergence within the inner loop.

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.

Examples

#decrease the maximum number inner iterations and display the 
     #evolution of the (outer) iterations
     seg.control(display = TRUE, maxit.glm=4)