Change-Points Estimation (with Possibly Random Effects)

seg.control

R Documentation

Auxiliary for controlling segmented/stepmented model fitting

Description

Auxiliary function as user interface for 'segmented' and 'stepmented' fitting. Typically only used when calling any 'segmented' or 'stepmented' method.

Usage

seg.control(n.boot=10, display = FALSE, tol = 1e-05, it.max = 30, fix.npsi=TRUE,
    K = 10, quant = FALSE, maxit.glm = 25, h = 1.25, break.boot=5, size.boot=NULL, 
    jt=FALSE, nonParam=TRUE, random=TRUE, seed=12345, fn.obj=NULL, digits=NULL, 
    conv.psi=FALSE, alpha = NULL, min.step=.0001, fc=.95)

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. Alternatively, see `selgmented`.
`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).
`break.boot`	Integer, less than `n.boot`. If `break.boot` consecutive bootstrap samples lead to the same objective function during the estimation process, the algorithm stops without performing all `n.boot` 'trials'. This can save computational time considerably. Default is `5` for the `segmented` and `5+2` for the `stepmented` functions.
`size.boot`	the size of the bootstrap samples. If `NULL`, it is taken equal to the actual sample size. If the sample is very large, the idea is to run bootstrap restarting using smaller bootstrap samples.
`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`. 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.
`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 values. The breakpoints are estimated within the quantiles `alpha[1]` and `alpha[2]` of the relevant covariate. If a single value is provided, it is assumed `alpha` and `1-alpha`. Defaults to `NULL` which means `alpha=max(.05, 1/n)`. Note: Proving `alpha=c(mean(x<=a),mean(x<=b))` means to constrain the breakpoint estimates within `[a,b]`.
`min.step`	optional. The minimum step size to break the iterative algorithm. Default to 0.0001.

fc

A proportionality factor (\le 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 paramete.

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.

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.

Author(s)

Vito Muggeo

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)

segmented documentation built on Nov. 28, 2023, 1:07 a.m.