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).
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)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.
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).
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. 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.
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 list with the arguments as components.
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.
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.
# NOT RUN {
#decrease the maximum number inner iterations and display the
#evolution of the (outer) iterations
seg.control(display = TRUE, maxit.glm=4)
# }
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